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Section 1: Foundation of the Study
Background of the Problem
The purpose of the research study was to explore the relationship between
emotional intelligence and sales performance. The sales team is an essential element in
the business-to-business selling process. For most companies, a salesperson initiates,
develops, and nurtures customer relationships (Kumar, Sunder, & Leone, 2014).
Business leaders worldwide spend billions of United States dollars every year training
their sales teams (Little, 2014). Business leaders can overlook the gap between mediocre
and high sales performance because productivity exists in both instances (Frino &
Desiderio, 2013). This performance gap can make a marked difference to the success of
a business and effect the development and income of its salespeople.
Emotional intelligence consists of using emotions to think more intelligently, and
has been identified by some researchers as having a positive relationship to workplace
success (Ono, Sachau, Deal, Englert, & Taylor, 2011). Understanding the relationship
between emotional intelligence and sales performance can lead to gap closure between
mediocre and high performance. Emotional intelligence can provide input into a more
thoughtful sales process with improved customer relationships at its epicenter (Borg &
Johnston, 2012). To gain a competitive advantage, and better organizational outcomes,
leaders should consider hiring and training for an emotionally intelligent salesforce.
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Problem Statement
United States business leaders spend $15 billion per year on sales training (Lassk,
Ingram, Kraus, & DiMasco, 2012), but approximately 50% of these businesses’
salespeople fail to reach their annual sales targets (Boichuk et al., 2014). An estimated
90% of top performers in virtually every industry possess high emotional intelligence
(Kidwell, Hardesty, Murtha, & Shibin, 2012), suggesting that high emotional intelligence
can be used to improve sales performance. The general business problem investigated by
this study is that companies are experiencing smaller returns on their training and
development investments in sales professionals. The specific business problem is that
some business leaders have a limited understanding of the relationship between the sales
performance of United States-based sales professionals and emotional intelligence and its
central constructs of self-perception, self-expression, interpersonal, decision making, and
stress management.
Purpose Statement
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. The independent variables tracked in this study were (a) emotional
intelligence, (b) self-perception, (c) self-expression, (d) interpersonal, (e) decision
making, and (f) stress management. The dependent variable was sales performance of
United States-based technology sales professionals. The targeted population consisted of
business-to-business technology sales professionals located throughout the United States.
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The intended business results of this study consist of developing emotional intelligence
sales training and recruitment programs that lead to higher sales quota attainment. These
findings have social implications for sales and business leaders who may use these results
to seek and hire emotionally intelligent sales professionals and train existing sales
professionals about emotional intelligence competencies to improve company-wide sales
performance.
Nature of the Study
A quantitative method was chosen for this study. Quantitative research seeks to
examine the significance of relationships or causes through numerical interpretation, not
to explore abilities or perceptions (Fisher & Stenner, 2011). The quantitative method was
appropriate for this study because the purpose of the study was to analyze numerical data
and infer the results to a larger population. The qualitative method is useful for
examining the attitudes held by individuals or similarities among participants (Schleifer
& Rothman, 2012), but would not have permitted testing whether emotional intelligence
varied with sales performance. A mixed-methods approach is beneficial when research is
designed to provide a comprehensive understanding of a problem or phenomenon
(Brannen & Moss, 2012). The qualitative and mixed-methods approaches were therefore
not deemed appropriate for this study. The decision to use a quantitative method came
from the need to evaluate the relationship between my independent and dependent
variables.
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A nonexperimental correlation design was chosen for this study. Correlation
research examines the presence and strength of relationships among covariates (Delost &
Nadder, 2014; Pilcher & Bedford, 2011). The correlation design was appropriate for this
study because the aim of this study was to understand the relationship between a set of
independent variables (emotional intelligence, self-perception, self-expression,
interpersonal, decision making, and stress management) and a dependent variable (sales
performance). Experimental design is the strongest of all research designs as it requires
manipulation, control, and random assignment (Delost & Nadder, 2014). Limited
resources prevented me from conducting an experimental design. Descriptive designs
study the existing state of a situation or circumstance (Bernard, 2013), which was not a
study goal. This research was aimed at understanding relationships among variables, so
experimental and descriptive designs were not appropriate.
Research Question
The overarching research question for this study was: What is the relationship
among emotional intelligence, self-perception, self-expression, interpersonal, decision
making, stress management, and sales performance?
Hypotheses
The six hypotheses proposed for this study included:
H10: There is no statistically significant relationship between a United States-
based technology sales professional’s emotional intelligence score and sales
performance.
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H1a: There is a statistically significant relationship between a United States-based
technology sales professional’s emotional intelligence score and sales
performance.
H20: There is no statistically significant relationship between a United States-
based technology sales professional’s self-perception composite score and sales
performance.
H2a: There is a statistically significant relationship between a United States-based
technology sales professional’s self-perception composite score and sales
performance.
H30: There is no statistically significant relationship between a United States-
based technology sales professional’s self-expression composite score and sales
performance.
H3a: There is a statistically significant relationship between a United States-based
technology sales professional’s self-expression composite score and sales
performance.
H40: There is no statistically significant relationship between a United States-
based technology sales professional’s interpersonal composite score and sales
performance.
H4a: There is a statistically significant relationship between a United States-based
technology sales professional’s interpersonal composite score and sales
performance.
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H50: There is no statistically significant relationship between a United States-
based technology sales professional’s decision making composite score and sales
performance.
H5a: There is a statistically significant relationship between a United States-based
technology sales professional’s decision making composite score and sales
performance.
H60: There is no statistically significant relationship between a United States-
based technology sales professional’s stress management composite score and
sales performance.
H6a: There is a statistically significant relationship between a United States-based
technology sales professional’s stress management composite score and sales
performance.
Theoretical Framework
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. Theories that framed this study were emotional intelligence and job
performance.
Emotional Intelligence Theory
During the 1970s and 1980s, psychologists Howard Gardner, Peter Salovey, and
John Mayer developed emotional intelligence theory. Three emotional intelligence
models exist (Samad, 2014). The ability-based model focuses on the individual's ability
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to process and use emotional information (Greenidge, Devonish, & Alleyne, 2014). The
trait-based model is measured by self-report and includes behavioral dispositions and
self-perceived abilities (Di Fabio & Saklofske, 2014). The mixed model combines
ability- and trait-based models. Emotional intelligence is an umbrella term that includes
a collection of personality traits, affect, and self-perceived abilities, rather than actual
aptitude (Joseph, Jin, Newman, & O’Boyle, 2015). As applied to this study, emotional
intelligence theory suggested that the independent variables of emotional intelligence
branches would influence sales performance outcomes because sales professionals rely
on emotional intelligence qualities. Figure 1 shows the emotional intelligence theory as it
applies to examining sales performance.
Figure 1. A graphical depiction of the emotional intelligence theoretical framework using
sales performance.
S
P
S -
P
S -
E
I
D
M
S
M
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Job Performance Theory
Campbell, McCloy, Oppler, and Sager (1993) created job performance theory.
Campbell et al. (1993) defined it as what people do that can be observed and measured
regarding proficiency or level of contribution. It also includes actions or behaviors
relevant to an organization’s goals (Blickle et al., 2011). Campbell et al. (1993) job
performance model is said to be the most prominent job performance model in the
literature (Borman, Brantley, & Hanson, 2014; Lee & Donohue, 2012). As applied to
this study, job performance theory suggested that the dependent variable of sales
performance would be unique to the population under study and not necessarily
generalizable to other populations.
Operational Definitions
Emotional intelligence: A person’s ability to determine their emotions and control
them, detect the emotional state of others, and leverage those emotions to influence them
(Farh, Seo, & Tesluk, 2012).
Emotional Quotient Inventory 2.0® (EQ-i 2.0®): A valid and reliable self-
assessment instrument to measure the total emotional
q
uotient (EQ) of an individual
(Multi-Health Systems, Inc., 2011). This assessment includes 133 items with a five-
point Likert scale response format. Its results include individual and workplace reports,
in addition to scores for emotional intelligence, its five composite scales, and 15
subscales.
Salesperson performance: The financial result of a salesperson's sales
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activities (
Valenzuela, Torres, Hidalgo, & Farías, 2014).
Assumptions, Limitations, and Delimitations
Assumptions
Assumptions are unverified facts that a researcher assumes are truthful (Martin &
Parmar, 2012). My initial assumption was that study participants would be candid
answering assessment questions and truthful providing demographic and sales
performance information (Leising, Locke, Kurzius, & Zimmermann, 2015). Differences
associated with mood, fatigue, and attention span did not taint results since these
individual differences were evenly distributed among respondents. The next assumption
was that study participants were representative of the population under investigation and
were normally distributed. Two United States-based technology sales population groups
were sampled. My company’s sales team, and my LinkedIn sales contacts participated.
An assumption was that study participants who achieved their most recent annual sales
performance target may not have a high level of emotional intelligence. My final
assumption was that conducting correlation and regression data analyses were appropriate
tests to address this quantitative study’s purpose statement.
Limitations
Every study has weaknesses (Bernard, 2013). My initial limitation was that each
research participant provided sales performance attainment specific to their employers’
sales expectations. Sales leaders employ a sales performance method that best meet a
company’s unique business needs, making generalizations to other businesses difficult.
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The next limitation was that research participants reported prior year sales attainment, as
opposed to, multi-year sales performance. Limited resources prevented gathering a
multi-year performance study. My company sales participants were encouraged, through
company leadership email, to participate in the optional emotional intelligence
assessment. Study participants may have felt compelled to score well and manipulate the
emotional intelligence assessment. Finally, the EQ-i 2.0® (Multi-Health Systems, Inc.,
2011) instrument is highly regarded as an emotional intelligence assessment tool. Other
instruments may exist that might be more advantageous for measuring emotional
intelligence such as the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT).
Delimitations
Bernard (2013) defined delimitations as the boundaries of a study. The scope of
this quantitative study was to examine the relationship between emotional intelligence
and sales performance of United States-based sales professionals. A boundary of this
study included that technology sales professionals were invited to participate through a
nonprobability purposeful sample from my company and my LinkedIn connections. As a
result, findings might only be applicable to those population samples, as opposed to other
sales organizations and industries.
Significance of the Study
Contribution to Business Practice
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
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sales professionals. Previous research studies were completed regarding emotional
intelligence and workplace performance (Cheng, Huang, Lee, & Ren, 2012; Gao, Shi,
Niu, & Wang, 2013; Mortan, Ripoll, Carvalho, & Bernal, 2014; Zampetakis &
Moustakis, 2011). No specific study examining whether a relationship exists between
emotional intelligence and sales performance of United States-based technology sales
professionals has been done.
By finding a meaningful correlation between levels of emotional intelligence and
sales performance in this study, support to develop and implement emotional intelligence
sales training and recruitment tools for businesses is enhanced (Haakonstad, 2011).
Business leaders are able to refine existing sales training and recruitment programs to
enhance sales performance. By understanding the strength of correlation between levels
of emotional intelligence and sales performance in this study, business leaders can
investigate other influential factors that affect sales performance more significantly than
emotional intelligence.
Implications for Social Change
This study was designed to promote positive social change by identifying
information for use in developing and implementing sales training and recruitment
programs that promote the wellness of sales professionals. By implementing effective
programs, sales professionals hired and trained have the desired skill sets to achieve
sales performance. Sales professionals learn to control their emotions more effectively
when dealing with customers and their companies. These outcomes lead to enhanced
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organization effectiveness, lower salesperson turnover, and higher sales performance
(Bande, Fernández-Ferrín, Varela, & Jaramillo, 2015). Employee performance
evaluations may not be consistent with an employee’s contribution to the company.
Emotional intelligence indicators should be an integral component of a more holistic
employee performance evaluation process (Pearman, 2011).
A Review of the Professional and Academic Literature
The purpose of this literature review was to explore previous emotional
intelligence and sales performance research and documentation. The literature review
explained how past researchers examined emotional intelligence and sales performance,
identified gaps in emotional intelligence and sales performance, and declared the need for
further investigative research. A variety of literature review methods and presentation
findings were found with an Internet search engine (Abrams, 2012). Search results aided
in organizing the information into logical segments.
The organization of this literature review begins with emotional intelligence
theory and background followed by an examination of emotional intelligence assessment
instruments. It includes an examination of emotional intelligence training and a
discussion of previous studies. It also includes a review of literature on sales
performance theory and its history, followed by a discussion of previously conducted
emotional intelligence and sales performance studies organized by industry segment.
The approach strategy for this literature review was to research English-language
peer-reviewed works from online databases, published works, and organization websites.
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Online research included scholarly journal and academic articles, reports, and influential
books. On-line databases included Thoreau, Academic Search Complete, ProQuest
Central, Business Source Complete, ABI/INFORM, Emerald Management Journals,
SAGE Premier, PsyncARTICLES, PsyncINFO, and Google Scholar.
The research for this study started with the primary keywords sales performance
and emotional intelligence. Further searches explored the following keywords: (a)
technology sales professional performance, (b) annual sales performance, (c) sales
outcomes, (d) sales training, (e) emotional intellect, (f) emotional aptitude and (g) EI.
The resulting research criteria produced more than 1,390 peer-reviewed articles and
books. By using both emotional intelligence and sales performance keywords, I was able
to further narrow results. Although older references provided the theoretical framework
for the study, more than 85% of the used references were peer-reviewed and published
within five years of my anticipated graduation date.
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. The independent variables were (a) emotional intelligence, (b) self-
perception, (c) self-expression, (d) interpersonal, (e) decision making, and (f) stress
management. The dependent variable was sales performance of United States-based
technology sales professionals. The targeted population consisted of business-to-business
technology sales professionals located throughout the United States.
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The overarching research question for this study was: What is the relationship
among emotional intelligence, self-perception, self-expression, interpersonal, decision
making, stress management, and sales performance?
The six hypotheses for this study were:
H10: There is no statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H1a: There is a statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H20: There is no statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H2a: There is a statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H30: There is no statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
H3a: There is a statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
H40: There is no statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
H4a: There is a statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
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H50: There is no statistical relationship between a United States-based technology
sales professional’s decision-making composite score and sales performance.
H5a: There is a statistical relationship between a United States-based technology
sales professional’s decision-making composite score and sales performance.
H60: There is no statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
H6a: There is a statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
Emotional Intelligence Theory
The concept of social intelligence first appeared in the work of Edward Thorndike
in the 1920s, and was defined as a type of intelligence a person could possess
(Birknerova, 2011). Emotional intelligence is considered an aspect of social intelligence
given social intelligence’s focus on relationships. Although ignored in 1940, David
Wechsler suggested including social intelligence as part of intelligence quotient (IQ)
testing (Faguy, 2012). In 1983, Howard Gardner proposed the theory of personal
intelligence. This theory included seven distinct types of intelligence, including intra-and
interpersonal intelligences (Ahuja, 2011; Brackett, Rivers, & Salovey, 2011; Ghraibeh,
2012). Intra-and interpersonal intelligences closely represent researchers’ current
understanding of emotional intelligence.
Emotional intelligence is the ability to appraise and express emotions, both one’s
own and those of others, and reflects an individual’s skill at interpreting a variety of
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verbal and nonverbal information. In 1990, Peter Salovey and John Mayer wrote what is
now considered to be the seminal work on emotional intelligence, an article entitled
Emotional intelligence (1990). Salovey and Mayer (1990) laid the groundwork for all
subsequent research and thinking about emotional intelligence (Faguy, 2012). These
researchers identified the relationship between emotions and workplace success and
developed Emotional Intelligence Theory, which states that emotions cue cognitive
capabilities and their range of management, or cognitive aptitude (Abe, 2011). Salovey
and Mayer (1997) further refined this definition of emotional intelligence as “the ability
to perceive accurately, appraise, and express emotion; the ability to access and/or
generate feelings when they facilitate thought; the ability to understand emotion and
emotional knowledge; and the ability to regulate emotions to promote emotional and
intellectual growth” (Salovey & Mayer, 1997, p. 35, as quoted in Faguy, 2012, p. 238).
Regulating or managing emotion can be routine, but an emotionally intelligent person
does it well.
In 1997, Mayer and Salovey revised their original conception to reflect four
branches of emotional intelligence. Perception, appraisal, and expression are part of the
basic emotional intelligence branches (Mayer, Salovey, & Caruso, 2004). This branch
reflects a fundamental ability to identify an emotion, including true versus false emotion,
as well as the ability to express emotions (Mayer et al., 2004). Emotional facilitation of
thinking has to do with the ability to use emotions in one’s thought, such as recalling an
emotion (Mayer et al., 2004). Understanding and analyzing emotions reflects a more
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sophisticated knowledge of emotions and how they operate, such as complex
combinations of emotions and emotional transitions (Mayer et al., 2004). Regulating
emotions relates to the ability to “detach” from emotions not immediately useful (Mayer
et al., 2004).
In 1995, while researching emotional literacy, Daniel Goleman read Salovey and
Mayer’s article and published a book titled Emotional Intelligence: Why It Can Matter
More Than IQ. It became a bestseller and helped to popularize emotional intelligence.
Goleman (1998) defined emotional intelligence as “the capacity for recognizing our own
feelings and those of others, for motivating ourselves, and for managing emotions well in
ourselves and in our relationships” (p 317). Goleman identified five competencies of
emotional intelligence including self-awareness, self-regulation, motivation, empathy,
and social skills (Goleman, 1998). Goleman explained how these five components or
talents mattered in work life. This framework has a basis for one’s ability to recognize a
feeling when it happens and possess the awareness to understand, control and apply this
emotion (Hess & Bacigalupo, 2011). As applied to this study, Goleman’s (1998) concept
of emotional intelligence theory was adopted.
Criticism regarding emotional intelligence theory encompasses two points of
view. The first is that emotional intelligence is not a new form of intelligence but rather
overlaps with existing constructs. Emotional intelligence was defined as a group of
qualities rather than a more precise scientific definition (Matthews, Zeidner, & Roberts,
2011). Although emotional intelligence instruments displayed good divergence from
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others, emotional intelligence questions showed high correlations with personality traits
(Matthews et al., 2011). The second criticism is that emotional intelligence is perceived
as having a desirable moral quality rather than a skill or outcome. Emotional intelligence
has been linked to positive outcomes in mental and physical health (O’Connor & Athota,
2013). Individuals who possess high self-perceived ability might be altruistic, but could
also be tempted to use that skill over others for self-gain (O’Connor & Athota, 2013).
Emotional Intelligence Instruments
As emotional intelligence gained attention from leading publications, advocates
promoted its use while skeptics questioned its legitimacy (O'Boyle, Humphrey, Pollack,
Hawver, & Story, 2011). As the debate continued on how important emotional
intelligence was to the workplace in the 1990s, practitioners conducted research into
methods of measuring it (Green & Salkind, 2011). Although some researchers
questioned the connection between emotionally intelligent leaders, and organizational
success (Lindebaum & Cartwright, 2011; Walter, Cole, & Humphrey, 2011), most of
emotional intelligence criticism surrounded the use of instruments to measure and predict
emotional intelligence.
Despite claims of the importance of emotional intelligence, empirical support for
its incremental direct effects on outcomes relevant to professional selling has been
disappointing (McFarland, Rode, & Shervani, 2015). Numerous emotional intelligence
instruments have been published with some producing inconsistent findings (Prentice &
King, 2012). Criticism surrounds some assessment tools that require self-reporting while
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other tools depend on evaluations by others. Participants may either intentionally
misrepresent themselves on self-assessments or may not be emotionally aware enough to
report on themselves accurately. Likewise, some participants may be too critical or
flattering of themselves in their self-ratings. Evaluations by others may also not be
accurate. Work colleagues could assess an inaccurately positive picture of each other
while other people might use the tool to retaliate against coworkers. Employees might
also be hesitant to be too critical of their supervisors or others in positions of power.
Finally, reports and assessments from multiple sources might be more accurate than a
single source, or might confuse the true assessment further (Faguy, 2012).
Three emotional intelligence models exist (Samad, 2014). The ability-based
model focuses on the individual's ability to process and use emotional information
(Greenidge et al., 2014). The trait-based model is measured by self-report and includes
behavioral dispositions and self-perceived abilities (Di Fabio & Saklofske, 2014). The
mixed model combines ability- and trait-based models. Emotional intelligence is an
umbrella term that includes a collection of personality traits, affect, and self-perceived
abilities, rather than actual aptitude (Joseph et al., 2015). As applied to this study,
emotional intelligence theory suggested that the independent variables of emotional
intelligence constructs would influence sales performance outcomes because sales
professionals rely on emotional intelligence qualities. Three of the more commonly
referred to emotional intelligence assessments include a trait-based model called
Emotional Quotient Inventory (EQ-i); a mixed-based model called Goleman’s Emotional
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Competency Index (ECI); and an ability-based model called Mayer-Salovey- Caruso
Emotional Intelligence Test (MSCEIT). An ability-based model for sales domain called
Emotional Intelligence Marketing Exchange (EIME) was also examined.
Emotional Quotient Inventory (EQ-i)
In 1996, Reuven Bar-On (2006) published the first emotional-social intelligence
(ESI) assessment tool and called it the Emotional Quotient-Inventory (EQ-i). Bar-On
(2006) defined ESI as “a cross-section of interrelated emotional and social competencies,
skills and facilitators that determine how effectively we understand and express
ourselves, understand others, and relate with them, and cope with daily demands” (p.3).
Bar-On coined the term Emotional Quotient (EQ) to refer to assessing emotional
competency. EQ-i is reported to be the most widely used emotional intelligence
assessment instrument (Multi-Health Systems, Inc., 2011). Given its ease to administer,
EQ-i was quickly accepted and implemented by organizations interested in an
inexpensive tool measuring potential for performance rather than performance itself (Bar-
On, 2006). The self-assessment can be administered online and takes less than 30
minutes to complete.
EQ-i measures emotional intelligence by a person’s ability with social behavior
traits and competencies (Bar-On, 2006). The self-assessment includes 133 questions and
incorporates a 5-point Likert scale response ranging from not true of me to true of me.
EQ-i spans five distinct areas including (a) intrapersonal skills, (b) interpersonal skills,
(c) adaptability, (d) stress management, and (e) general mood (Bar-On, 2006).
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Intrapersonal refers to the inner self and evaluates self-regard, emotional self-awareness,
assertiveness, independence, and self-actualization (Bar-On, 2006, p. 4). Interpersonal
measures capacity and functioning, such as empathy, social responsibility, and
interpersonal relationships (Bar-On, 2006, p. 4). Stress management measures tolerance
and impulse control (Bar-On, 2006, p. 4). Adaptability measures reality testing,
flexibility, and problem-solving skills, as well as, how a person copes with environmental
demands (Bar-On, 2006, p. 4). General mood consists of optimism and happiness and
measures one’s general feeling of content and outlook on life (Bar-On, 2006, p. 4).
Based on 4,000 North American respondents who were majority younger than 30, EQ-i
was reported to have equal representation of men and women (Bar-On, 2006). Reliability
and validity of the assessment have been tested and reported (Bar-On, 2006).
A revised EQ-i 2.0® was developed and included updated scales based on
information gathered from ongoing research (Multi-Health Systems, Inc., 2011). EQ-i
2.0® included changes in: (a) intrapersonal EQ composite was divided into two separate
composite scales, self-perception and self-expression; (b) emotional expression was a
new subscale added to the self-expression composite scale, and included both verbal and
nonverbal expression; (c) problem-solving was redefined to avoid interpretation issues on
the EQ-i; (d) decision making composite scale was added and includes problem-solving,
reality testing, and impulse control; (e) happiness was a general mood indicator on the
EQ-i and contributed to the Total EQ score but was moved to a wellness indicator and
does not affect the Total EQ score (Multi-Health Systems, Inc., 2011).
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Scoring used in the EQ-i 2.0® includes raw scores that are converted to adjusted
scores. Based on normative data from over 4,000 individuals, scores are reflected in a
number ranging from 50–150 (Multi-Health Systems, Inc., 2011). A score lower than 70
indicates a very much below average EQ score. A score between 71 and 90 suggests a
below average rating. A score between 91 and 110 reflects an average level of EQ. A
score between 111 and 130 indicates an above average EQ. Any score over 130 suggests
an individual has a very much above average EQ. The EQ-i 2.0® provides a total
emotional intelligence score, based on the self-perception, self- expression, interpersonal,
decision making, and stress management composite scale scores (Multi-Health Systems,
Inc., 2011). As applied to this study, emotional intelligence theory suggested that the
independent variables of emotional intelligence constructs would influence sales
performance outcomes because sales professionals rely on emotional intelligence
qualities.
Emotional and Social Competence Inventory (ESCI)
Developed in 1991 by Boyatzis and Goleman, the Emotional-Social Competence
Inventory (ESCI) was designed to assess emotional competencies and positive social
behaviors (Shanmugasundaram & Mohamad, 2011). ESCI is a 360-degree assessment
that relies on others’ assessments of an individual’s emotional intelligence (Faguy, 2012).
ESCI used a 7-point Likert rating scale to rate characteristics across 18 competencies of
the person being assessed (Shanmugasundaram & Mohamad, 2011).
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Revised by Goleman and Boyatzis in 2007, ESCI version 3 uses a 5-point Likert
rating scale with 68 questions across four domain clusters including (a) self-awareness,
(b) self-management, (c) social awareness, and (d) relationship management
(Shanmugasundaram & Mohamad, 2011). Self-awareness competency is defined as
“emotional self-awareness” (Shanmugasundaram & Mohamad, 2011, p. 1792). Self-
management competencies refer to “achievement orientation, adaptability, emotional
self-control, and positive outlook” (Shanmugasundaram & Mohamad, 2011, p. 1792).
Social-awareness competencies include “empathy and organizational awareness”
(Shanmugasundaram & Mohamad, 2011, p. 1792). Relationship management
competencies refer to “conflict management, coach and mentor, influence, inspirational
leadership, and teamwork” (Shanmugasundaram & Mohamad, 2011, p. 1792). ESCI is
often used to assess managerial abilities given its reliance on others’ assessments of an
individual’s emotional intelligence.
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT)
The Multifactor Emotional Intelligence Scale (MEIS), the predecessor to the
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), was first developed in
the mid-1990s by the team of Mayer, Salovey, and Caruso. MEIS analyzed a
participant's emotional intelligence by assessing their ability to identify, understand, and
manage their emotions and identify emotions of others. Responses to the MEIS
assessment were measured by experts. In 2002, the MEIS was refined and grew into the
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT).
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MSCEIT is a performance based measurement regarding how people perform
tasks and solve emotional problems (Mayer, Salovey, & Caruso, 2002). MSCEIT was
developed using a sample population of 5,000 participants worldwide, including mostly
white women younger than 30 (Mayer et al., 2002). Test format comprises 141 questions
that can be administered to individuals or in a group setting, through paper or on-line
(Mayer, Salovey, & Caruso, 2012). Assessment takes less than 45 minutes to complete.
Responses on the MSCEIT are unaffected by self-concept or other biases. MSCEIT
assessment yields an emotional intelligence score, two area scores, and four branch
scores grounded within individual branches (Mayer et al., 2012). Each branch comprises
specific tasks that the respondent must complete to generate two domain scores (Mayer et
al., 2012).
The MSCEIT measures emotional intelligence across four scales including (a)
perceiving emotion, (b) facilitating thought, (c) understanding emotion, and (d) managing
emotions (Mayer et al, 2002). Perceiving emotion concerns “identifying emotions
conveyed through expressions and abstract pictures” (Fiori & Antonakis, 2011, p. 6).
Facilitating thought refers to “how certain moods may facilitate thinking and comparison
of emotions to sensations, such as color, light, and temperature” (Fiori & Antonakis,
2011, p. 6). Understanding emotion concerns “connecting emotions to certain situations
and knowledge of how emotions may change and develop” (Fiori & Antonakis, 2011, p.
6). Managing emotions refers to “rating which emotional strategy would be most
appropriate to handle a situation and be effective for self-regulation” (Fiori & Antonakis,
25
2011, p. 6). Previous studies have demonstrated that the MSCEIT provides reliability in
assessment scores (Mayer et al., 2012).
Emotional Intelligence Marketing Exchange (EIME)
One’s ability to recognize, regulate, and use emotional information can result in
highly effective performance (Emmerling & Boyatzis, 2012). Rather than competing
with general domain emotional intelligence assessments, Kidwell, Hardesty, Murtha, and
Sheng (2011) published a study focused on creating a new emotional intelligence
assessment specifically targeted toward salespeople. Emotion Intelligence Marketing
Exchange (EIME) assessment focused on ability-based, domain-specific criteria such as
sales revenue and customer retention (Kidwell et al., 2011).
Emotion Intelligence Marketing Exchange (EIME) scale was designed to identify
unique emotional abilities that make salespeople more effective when selling products
and services to their customers. EIME assessment measures four dimensions including
(a) perceiving emotion, (b) facilitating (or using) emotion, (c) understanding emotion,
and (d) managing emotion (Kidwell et al., 2012). Format comprises 15 questions
administered to individuals through paper or on-line (Kidwell et al., 2011)
Emotional Intelligence Training
Emotional intelligence describes characteristics beyond technical skill and
traditional cognitive intelligence. It includes factors like awareness of and ability to
regulate emotional responses and to understand others. Employees who possess higher
emotional intelligence than others seem to manage stress and other workplace issues
26
better (Jordan & Troth, 2011). Employee attitudes can be changed; all aspects of
emotional intelligence can be developed and improved (Jahangard et al., 2012).
Researchers in various industries have investigated whether it is possible to
increase emotional intelligence. According to Kidwell et al. (2012), salespeople with
moderate to high cognitive intelligence stand a better chance of learning to improve their
emotional intelligence skills. Such training could help sales professionals improve their
performance. Business leaders could assess overall emotional intelligence competencies
to discern which dimensions are lacking and focus training on overcoming emotional
intelligence weaknesses (Kidwell et al., 2012). The Consortium for Research on
Emotional Intelligence in Organizations (2014) recommend four phases of corporate
emotional intelligence training including (a) preparation, (b) training, (c)
transfer/maintenance, and (d) evaluation.
Previous studies comparing emotional intelligence trained groups with nontrained
groups found that training increased emotional intelligence competency (Abe, 2011; Ono
et al., 2011; Schutte, Malouff, & Thorsteinsson, 2013). For example, Gignac, Harmer,
Jennings, and Palmer (2012) examined the effectiveness of an emotional intelligence
training program on sales performance of pharmaceutical sales representatives from
Australia. Using the self-report Genos Emotional Intelligence Inventory assessment, an
experimental, repeated measures between-groups design was used (Gignac et al., 2012).
Results demonstrated that rater-report emotional intelligence correlated significantly with
sales performance (Gignac et al., 2012). Salespeople who received emotional
27
intelligence training “outperformed a corresponding control group by approximately 9%
with respect to sales performance” (Gignac et al., 2012, p. 104). Studies were also
reported by Nelis et al. (2011) who observed higher emotional intelligence achievement
among participants after brief training on emotional intelligence competencies.
Kirk, Schutte, and Hine (2011) provided emotional intelligence, emotional self-
efficacy, and workplace civility training to employees. Participants were segmented into
intervention and control groups. Participants in the intervention group showed significant
increases in self-efficacy following training. Employees scored higher on emotional
intelligence, emotional self-efficacy, and workplace civility than employees in the control
group.
Kotsou, Nelis, Gregoire, and Mikolajczak (2011) reported higher than expected
increases in self-reported and observer-reported emotional intelligence among 132
participants in their intervention group than among participants in their control group.
The intervention group was trained on five core emotional competencies. Emotional
intelligence skills increased significantly in the intervention group. Participants also
showed higher increases in life satisfaction and lowered self-reported stress levels.
Kotsou et al (2011) revealed that emotional intelligence competencies can be improved
and have lasting personal benefits.
Not all studies support that emotional intelligence training improves work-related
outcomes. For example, Larin, Benson, Wessel, Martin, and Ploeg (2013) compared the
development of emotional-social intelligence (ESI) of nursing and physical therapy
28
students. From the beginning of their education until after their first clinical experience,
73 nursing students and 60 physical therapy students completed self-report
questionnaires. Bar-On’s Emotional Quotient Inventory Short survey instrument was
used. Results revealed that participants had little change.
Kruml and Yockey (2011) also concluded that there were no significant
differences in the effectiveness of a seven-or 16-week emotional intelligence curriculum.
Participants who initially scored low or had average emotional intelligence scores
experienced some improvements. Others who initially scored high did not see an
improvement in their emotional intelligence score.
Job Performance Theory
Campbell et al. (1993) created job performance theory. They defined job
performance as what people do that can be observed and measured regarding proficiency
or level of contribution. It also includes actions or behaviors relevant to an
organization’s goals (Blickle et al., 2011). As an outcome, salesperson job performance
is defined as the financial result of a salesperson's sales activities (Valenzuela et al.,
2014). Campbell’s job performance model is said to be the most prominent job
performance model in the literature (Borman et al., 2014; Lee & Donohue, 2012). The
ability to predict sales job performance remains elusive due to many variables including
salesperson selection, buyer–seller interactions, job design, incentive systems, sales
controls, and supervision (Evans, McFarland, Dietz, & Jaramillo, 2012).
29
Emotional intelligence competencies include being aware of emotions and using
them to guide thinking and behavior (Abe, 2011; Agnihotri, Krush, & Singh, 2012;
Coleman, 2014). Emotional intelligence is considered a strong predictor of success in the
workplace (Nadler, 2011). Many early studies on sales job performance focused on the
strength of relationship between salesperson qualifications (e.g., years of selling
experience) and sales attainment (Ross, Desiderio, Knudstrup, & Frino, 2013). From the
mid-twentieth century, sales job performance evolved from comparisons to job
satisfaction and strategy to more subjective concepts such as communication, context,
and emotions. In frameworks and various literature reviews, sales performance has been
conceptualized to be the result of a collection of moderating and mediating variables.
Performance measures should include factors influencing work-related outcomes
(Bateman & Snell, 2012). Medhurst and Albrecht (2011) indicated that sales
performance was an important element regarding individual and organizational
performance. They presented an individual-level salesperson job performance model
regarding (a) how employee involvement climate influences engagement, (b) how
psychological capital influences performance, (c) how employee involvement climate and
psychological capital interact to influence employee engagement; and (d) how, in turn,
engagement influences salesperson performance (Medhurst & Albrecht, 2011, p. 398).
Researchers expect their model to be useful to human resource and sales managers
looking to improve skills and more fully involve salespeople to optimize salesperson
performance (Medhurst & Albrecht, 2011).
30
Given the growing global competitive pressure, extensive research has been done
to understand the most influential factors of sales job performance (Bodla & Naeem,
2014; Verbeke, Dietz, & Verwaal, 2011). Intrinsic and extrinsic motivation has been
thoroughly explored in leading sales and marketing journals (Verbeke et al., 2011).
Motivation is a top predictor of sales job performance in meta-analytical reviews
(Verbeke, et al., 2011). For example, Bodla and Naeem (2014) developed and tested a
“theory-driven framework in linking intrinsic motivation to sales job performance while
using sales force creative performance as a partial mediator” (Bodla & Naeem, 2014, p.
468). Results concluded that sales performance was encouraged by intrinsic motivation
(Bodla & Naeem, 2014).
Wanting to understand if a salespersons’ job performance was related to their
ability to be coached, Shannahan, Bush, and Shannahan (2013) indicated that sales
performance was “highest when salespeople are highly coachable, highly competitive,
and under transformational leadership” (Shannahan et al., 2013, p. 40). Salespersons
acceptance of being coached was an important mediator to both transformational
leadership and competitiveness impact to sales job performance (Shannahan et al., 2013).
As company leadership spends millions per year to increase salesperson job
performance and reduce salesperson turnover (Johnston & Marshall, 2013), a way to
accomplish both is through company mentoring programs. Rollins, Rutherford, and
Nickell (2014) study focus explored informal mentoring on outcome-based salesperson
performance. Mentoring is believed to play a significant role in corporations, yet little
31
empirical evidence exists on its influence on salesperson job performance. Researchers
interviewed salespeople of an international insurance company regarding mentoring and
sales job performance. Using a qualitative research approach, Rollins et al. (2013)
explored a mentor’s influence on the sales job performance of the apprentice. Findings
suggested that mentoring contributes to salesperson job performance in numerous ways
(Rollins et al., 2014).
Guidice and Mero (2012) examined whether feedback on sales job performance
was an accurate predictor to manager ratings. By conducting a study of 167 salespeople,
results revealed that sales job performance and ratings of interpersonal facilitation was
moderated only by the salespeople’s political skill (Guidice & Mero, 2012). Sales job
performance and manager ratings of task performance were related, interpersonal
facilitation was negatively related to sales job performance (Guidice & Mero, 2012).
This suggests that those who hedged their bets were less likely to achieve future sales
goals (Guidice & Mero, 2012).
Related Studies
The 21st century brings a continual wave of constantly changing and developing
technologies that influence the way people communicate, interact, and receive
information (Grewal & Levy, 2012; Hughes, Bon, & Rapp, 2013). The effect such
evolution of change will have on the emotional intelligence process in sales is yet to be
fully understood (Grewal & Levy, 2012). As a framework, emotional intelligence has
been widely researched with application in various fields and occupations (Ahmetoglu,
32
Leutner, & Chamorro-Premuzic, 2011; Borg & Freytag, 2012). Researchers have
acknowledged the importance of emotional intelligence on job performance across its
eighteen competencies. Nadler (2011) believed that effective management of emotions
could even promote more successful professional endeavors.
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. The independent variables were (a) emotional intelligence, (b) self-
perception, (c) self-expression, (d) interpersonal, (e) decision making, and (f) stress
management. The dependent variable was sales performance of United States-based
technology sales professionals. The targeted population consisted of business-to-business
technology sales professionals located throughout the United States. Since 2011, many
quantitative correlational studies provided informative research comparing emotional
intelligence and sales job performance. While many of these studies were completed in
the financial services and healthcare industries, none were identified in the technology
sector.
Financial Services Industry
Within the financial services sector, Enhelder (2011) investigated the relationship
between emotional intelligence and sales performance among 717 financial advisors. All
were employed at one large financial services firm and completed the Bar-On Emotional
Quotient-Inventory (EQ-i) assessment. Results demonstrated a statistically significant
relationship between emotional intelligence and financial advisor sales performance
33
(Enhelder, 2011). Researcher posited that firms employing financial advisors might want
to use emotional intelligence assessments to predict future sales performance of job
applicants (Enhelder, 2011). Using emotional intelligence results may also help develop
emotional intelligence competencies of current staff (Enhelder, 2011).
Boyatzis, Good, and Massa (2012) assessed the level of emotional and social
intelligence (ESI) competencies on sales leader performance within the financial services
industry. Results indicated that ESI was a good predictor regarding leader effectiveness
(Boyatzis et al., 2012). Specifically, adaptability and influence competencies predicted
sales leadership performance (Boyatzis et al., 2012).
Successful sales professionals use emotions to enable positive outcomes for
themselves and their customers. Kidwell et al. (2012) posited that emotions play a
significant role in managing buyer-seller relationships. Three field studies were
conducted to examine the influence of emotional intelligence on sales performance and
customer relationships (Kidwell et al., 2012). Using their own emotional intelligence
assessment tool, Emotional Intelligence in Marketing Exchange (EIME), researchers
concluded that emotional intelligence had a significant relationship to the performance of
real estate and insurance agents (Kidwell et al., 2012). Findings supported the
supposition that sales professionals with higher emotional intelligence were superior
revenue generators and better at retaining customers (Kidwell et al., 2012). Other results
indicated a performance relationship exists between emotional intelligence and higher
levels of cognitive ability (Kidwell et al., 2012).
34
Assessing the level of understanding of emotional intelligence among real estate
professionals, Swanson and Zobisch (2014) identified 18 licensed realtors through
LinkedIn and Facebook, who responded to 17 questions. Survey results concluded that
“an awareness of emotional intelligence among licensed real estate professionals exists,
and realtors could be trained on the topic of emotional intelligence” (Swanson & Zobisch,
2014, p. 9). Findings also reflected a relationship existed between emotional intelligence
and realtor extrinsic motivation, namely economic rewards and client satisfaction
(Swanson & Zobisch, 2014).
Haakonstad (2011) also studied the predictive relationship between emotional
intelligence and sales performance of real estate professionals but had different results.
Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), Haakonstad
(2011) tested 75 real estate professionals. Results indicated that emotional intelligence
did not relate statistically to sales performance, and that MSCEIT four branch scales did
not explain a significant proportion of variance in sales performance (Haakonstad, 2011).
Using Emotions subscale, did relate more strongly to sales performance than any other
subscale.
Kidwell et al. (2011) proposed that the use of emotions by emotionally intelligent
salespeople could affect buyer-seller exchanges in a positive way leading to increased
performance. Kidwell et al. (2011) measured the levels of emotional intelligence and
intellect of real estate and insurance salespeople in relation to their marketing exchange
variables. Findings concluded that variables, such as customer orientation and influence
35
in the execution of sales, were higher with salespeople that had more effective emotional
quotients (Kidwell et al., 2011). Indicating a relationship between high cognitive ability
and high emotional intelligence, the results could be positively applied regarding
selection, training, and improving customer interactions (Kidwell et al., 2011).
Healthcare Industry
Within the healthcare industry, Griffin (2013) investigated the relationship of 108
pharmaceutical sales managers’ sales performance to their emotional intelligence. Sales
performance was defined as the percent of annually attained sales goals compared to a
predetermined sales objective. Measured using the Mayer-Salovey-Caruso Emotional
Intelligence Test (MSCEIT) 2.0 of emotional intelligence, results suggested that Branch 3
of emotional intelligence, Understanding Emotions, was a significant predictor of a sales
managers' sales performance (Griffin, 2013).
Staying within the pharmaceutical sector, Billings (2012) evaluated which
psychological and related cognitive characteristics predicted sales performance of 173
pharmaceutical representatives. Using the Emotional Intelligence Marketing Exchange
(EIME) instrument and the Hermann Brain Dominance Instrument, results revealed a
relationship between thinking style and emotional intelligence working together to
increase sales performance (Billings, 2012). This link between thinking styles and
emotional intelligence working together only occurred for study participants who were
new to pharmaceutical sales (Billings, 2012).
36
Wanting to understand the relationship between 112 district sales managers'
emotional intelligence and their behavioral style at bio-pharmaceutical company,
Megowan (2012) used the DiSC® Classic 2.0 assessment to measure district sales
managers' behavioral style, and the Bar-On Emotional Quotient Inventory (EQ-i) survey
instrument. Results of the study did not demonstrate any direct correlation between
leadership behavioral style and the corresponding level of emotional intelligence among
district sales managers (Megowan, 2012). Assessing the emotional and social
intelligence competencies of 115 sales professionals at a life sciences company and
comparing results to participants’ sales performance to determine if a correlation exists,
Lisicki (2011) administered the Emotional Social Competency Inventory (ESCI)
assessment. Results indicated a relationship between emotional intelligence toward job
satisfaction and sales performance (Lisicki, 2011).
Farnham (2012) evaluated the relationship between sales performance and
emotional intelligence of 35 hospice sales professionals employed by a regional hospice
organization. Using the Bar-On Emotional Quotient Inventory (EQ-i) survey instrument,
results indicated a relationship between emotional intelligence and hospice sales
performance (Farnham, 2012). Specifically, each additional 1-point in EQ-i total score
was associated with a .316-unit increase in sales (Farnham, 2012). Gender and tenure
were determined to have no significant relationship with hospice sales performance
(Farnham, 2012). Wanting to explore the relationship between emotional intelligence
and self-reported sales performance of medical equipment sales representatives in the
37
United States, Harris, Mirabella, and Murphy (2012) assessed 136 participating sales
representatives who had at least 12 months tenure at a medical equipment manufacturer.
Using the Bar-On Emotional Quotient Inventory (EQ-i) survey instrument, results
indicated that there were differences in emotional intelligence scores by gender and
tenure, but there was no conclusive relationship between emotional intelligence and sales
performance (Harris et al., 2012).
Other Industries
Within the call center environment, Shamsuddin and Rahman (2014) investigated
the relationship between emotional intelligence (independent variable) and job
performance (dependent variable) of 118 call center agents in Malaysia. Researchers
used the self-report Wong and Law Emotional Intelligence Scale and discovered that
there was a relationship between emotional intelligence and job performance
(Shamsuddin & Rahman, 2014). Emotional intelligence dimensions of “Regulation
Appraisal Emotion and Use of Emotion contributed significantly to job performance”
(Shamsuddin & Rahman, 2014, p. 75). Wanting to understand the relationship between
emotional intelligence and individual inbound call center performance of 17 agents,
Gahan (2012) administered the Bar-On Emotional Quotient Inventory (EQ-i) assessment.
Call center performance metrics included percent sales attainment, employee’s monthly
average deal size, employee’s quality score, and adherence to assigned work schedule
(Gahan, 2012). Researcher concluded that findings were inconclusive primarily from
small sample size (Gahan, 2012).
38
Within Puerto Rico, De La Cruz, D’Urso, and Ellison (2014) evaluated if
statistical significance existed between emotional intelligence and successful sales
performance of 103 participants living in Puerto Rico. Using the Global Emotional
Intelligence Test, results indicated that emotional intelligence had a statistical
relationship to sales performance (De La Cruz et al., 2014). Correlation between
variables was moderate (De La Cruz et al., 2014). Results also concluded that
moderating variables did not produce any significant variation from original results (De
La Cruz et al., 2014). Researchers generalized that emotional intelligence was an
important yet not a determinant factor for sales success in Puerto Rico (De La Cruz et al.,
2014).
Within retail sales, evaluating if 112 participating home furniture retail sellers
emotional intelligence contributed to their sales performance, Giorgi, Mancuso, and Fiz
Perez (2014) administered the Organizational Emotional Intelligence Questionnaire
(ORG-EIQ). This survey instrument consisted of 99 questions that assessed emotional
and organizational competencies using a self-report method (Giorgi et al., 2014). After
four months, participants’ sales results were compared with other criteria. Findings
showed a significant relationship between emotional intelligence and top performers
(Giorgi et al., 2014). Results also indicated that emotional intelligence skills were
relevant in association with job performance, particularly relationship management and
self-management branches (Giorgi et al., 2014).
39
Within higher education, studying 175 participating Spanish students from three
universities, Sánchez-Ruiz, Hernández-Torrano, Pérez-González, Batey, and Petrides
(2011) investigated the association between creativity, cognitive ability, personality, and
trait emotional intelligence. Results concluded that strong relationships existed between
creativity and emotional intelligence (Sánchez-Ruiz et al., 2011). Within recruiting
services, Downey, Lee, and Stough (2011) administered the Swinburne University
Emotional Intelligence Test to 100 participants in an Australian professional recruitment
company to understand whether financial revenue performance earned were more
strongly related to emotional intelligence rather than measures of intelligence quotient
(IQ) and personality. Results concluded that emotional intelligence and personality were
predictors of job performance with emotional intelligence being a strong indicator of job
performance (Downey et al., 2011).
Within retail sales, Moon and Hur (2011) studied the ways in which emotional
intelligence affected emotional exhaustion that could influence organizational
commitment, job satisfaction, and job performance among 295 participating retail sales
employees in South Korea. Using the Schutte Self-Report Emotional Intelligence Test,
results indicated that employees’ emotions, optimism levels, and social skills were
negatively linked with emotional exhaustion (Moon & Hur, 2011). Emotional exhaustion
was also found to be negatively linked to job performance (Moon & Hur, 2011).
Within the media industry, Roy and Chaturvedi (2011) evaluated the relationship
that age and work experience had to emotional intelligence and performance of 270
40
participants from print media companies. Using the Emotional and Social Competence
Inventory assessment, researchers divided participants into age and work experience
groups to correlate these with the dependent variable of emotional intelligence. Results
revealed that there was a significance in both age and work experience in relation to the
level of emotional intelligence (Roy & Chaturvedi, 2011). Researchers commented that a
peak of emotional intelligence was observed in people in the age group above 40 (Roy &
Chaturvedi, 2011).
Wanting to understand if a correlation existed between emotional intelligence,
transactional or transformational leadership styles, and sales performance, Brown (2014)
investigated emotional intelligence and leadership styles on sales performance. Brown
(2014) provided a descriptive analysis of literature that led to a conceptualized model of
leadership style, emotional intelligence, and sales performance. Results suggested that a
relationship exists between leadership style, emotional intelligence, and sales
performance” (Brown, 2014).
Within the private sector, Chaudhry and Usman (2011) studied the association
between emotional intelligence and job performance on 444 participating employees
working in privately owned organizations. Emotional intelligence was measured through
a self-reporting Likert scale consisting of 33 items while employee job performance was
measured through a self-reporting Likert scale of 16 items (Chaudhry & Usman, 2011).
Results exposed a significant relationship between emotional intelligence and job
performance (Chaudhry & Usman, 2011). Researchers concluded that job performance
41
could be predicted based upon emotional intelligence scores; the use of an emotional
intelligence assessment could be used to augment employee selection by human resource
managers (Chaudhry & Usman, 2011).
Within national sales organizations, Russell and Walker (2011) hypothesized that
salespeople possessing high emotional intelligence were more successful than their low
emotional intelligence counterparts. Using the Schutte Self-Report Emotional
Intelligence Test, researchers administered the assessment to 24 sales professionals and
managers. Sales performance was measured by both self- and peer reports. Results
concluded that emotional intelligence was positively related to sales performance; those
salespeople with the highest emotional intelligence levels were found to have higher sales
performance (Russell & Walker, 2011).
Wanting to understand the effect of emotional intelligence on the relationship of
adaptive selling and customer loyalty to the salesperson, Chen (2011) analyzed the
accumulated knowledge and explored research gaps in empirical sales research. Chen
supposed that even though some studies proposed no link between emotional intelligence
to sales performance, the prevailing view was that a higher level of emotional intelligence
would have a positive effect on sales performance. Chen reviewed the use of emotional
intelligence by salespeople not from the salesperson’s point of view but rather the point
of view of the customer. Findings indicated that, even though, little to no influence from
the salesperson’s viewpoint to the customer relationship was noted, an adverse effect
from the customer point of view might have existed (Chen, 2011). Control of emotions
42
may have provided the opportunity for sales personnel to be more direct and pragmatic
with customers. The lack of emotional or empathetic relationship with the customer may
have affected the process in a negative way (Chen, 2011). Wanting to understand if there
was a correlation between organizational citizenship behavior and emotional intelligence,
Yaghoubi, Mashinchi, and Hadi (2011) revealed that a relationship exists between
emotional intelligence and organizational citizenship behavior. This outcome indicated
that emotional intelligence could have an influence on organizational development by
creating a stronger consciousness of the organizational exchange among its citizens
(Yaghoubi et al., 2011).
The debate over emotional intelligence’s influence on job performance continues
despite almost two decades of research about the topic. While many researchers provide
statistical proof that emotional intelligence has an effect on job performance, others have
revealed no statistical significance on job performance. Using a meta-analysis
framework, Zhang and Wang (2011) assessed the influence of emotional intelligence on
job performance. Studies evaluated were written in English and Chinese and conducted
from 1990 to 2009. Results indicated that the relationship between emotional
intelligence and job performance was moderately strong (Zhang & Wang, 2011).
Literature Review Conclusion
A review of the academic and professional literature served as the basis for the
theoretical framework and variables used in this study. The review consisted of an
examination of emotional intelligence and job performance theories, popular emotional
43
intelligence instruments, and emotional intelligence training. Since 2011, researchers’
published six quantitative studies that studied the measurable results of emotional
intelligence training. Researchers also published six quantitative studies since 2011 that
studied sales job performance and the factors that influence positive results. Since 2011,
researchers’ published 25 quantitative studies investigating the relationship between
emotional intelligence and sales performance with 20% reporting the use of Bar-On’s
Emotional Quotient-Inventory (EQ-i). Since 2011, no researchers have published studies
evaluating the relationship between emotional intelligence and sales performance of
technology sales professionals. The literature review was useful to highlight trends in
research methods and benefits of academic research to bridge the gaps in knowledge for
business leaders.
Transition
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. Section 1 established the foundation or basis for the study and
included the background, business problem, purpose statement, research questions, and
theoretical framework. The literature review explored the existing body of knowledge
and included fundamental findings between emotional intelligence and sales
performance.
Section 2 outlines the role of the researcher, lists eligibility criteria for
participants and describes the research method and design. Target population is named,
44
and sample size is calculated. Consideration and compliance to ethical standards are
reviewed. Data collection instruments and techniques are described as are data analyses
proposed methods. Section 2 concludes with a discussion on study reliability and
validity. Section 3 includes presentation of the findings, a discussion regarding the
applicability to professional practice, the implications for social change,
recommendations for action and further research, reflections, and the conclusion of the
study.
45
Section 2: The Project
This section outlines my role as researcher, lists the eligibility criteria for study
participants, and describes the selected research method and design. It includes a
description of my target population and shows the calculations that I made to determine
an appropriate sample size. It also contains a review of the steps taken to consider and
comply with ethical standards, and descriptions of the data collection instruments used.
Section 2 concludes with a discussion on this study’s reliability and validity.
Purpose Statement
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. The independent variables were (a) emotional intelligence, (b) self-
perception, (c) self-expression, (d) interpersonal, (e) decision making, and (f) stress
management. The dependent variable was sales performance of United States-based
technology sales professionals. The targeted population consisted of business-to-business
technology sales professionals located throughout the United States. The intended
business results of this study consisted of developing emotional intelligence sales training
and recruitment programs that lead to higher sales quota attainment. These findings have
social implications for sales and business leaders who may use these results to seek and
hire emotionally intelligent sales professionals and train existing sales professionals about
emotional intelligence competencies to improve company-wide sales performance.
46
Role of the Researcher
For quantitative research, the role of the researcher is to advance a theory, collect
data to test, and report the confirmation or disconfirmation of the results (Bansel &
Corley, 2012). My role as the researcher was to maintain objectivity in the data
collection and analysis processes and to abide by ethical research practices (Bernard,
2013). This research study design evolved from an extensive review of academic and
professional literature, and my familiarity with technology sales having participated in
the industry for more than twenty years. Two United States-based technology sales
population groups were sampled. My company’s sales team, and my LinkedIn sales
contacts participated. None of the technology sales professionals employed by my
company, a $1.5 billion information technology services firm with 20,000 employees,
worked for me directly. I did have interaction with many of them as the leader of a
business segment.
I took several steps to ensure that I followed ethical guidelines to alleviate the
risks of doing research in one’s place of employment, as suggested by Hofmeyer, Scott,
and Lagendyk (2012). For example, I took steps to ensure participant anonymity and
separated myself from the data collection process to mitigate any potential bias in the
survey process, per the recommendations of Menachemi (2011). I collected the company
study data through an online demographic survey and emotional intelligence assessment
named EQ-i 2.0® developed by Multi-Health Systems, Inc. (2011), to make sure that a
strict adherence to participant confidentiality was observed. The company that I worked
47
for provided permission to sample their technology sales professionals (see Appendix A),
and Multi-Health Systems, Inc. (MHS) provided permission to use their survey
instrument (see Appendix B).
Wester (2011) indicated researchers must be cognizant of ethical issues that may
arise during the research process. The Belmont Report summarized the ethical principles
set forth in the 1974 National Research Act (Pub. L. 93-348), which governs the
standards and acceptable practices when researching human subjects. Researchers must
have respect for persons, beneficence, and justice. Because human subjects participated
in this research study, the Walden University Institutional Review Board (IRB) required
submission of an application for research ethics review. On May 21, 2015 IRB approval
number 05-21-15-0244380 was provided (see Appendix C). The following day, data
collection began and lasted for two weeks.
Participants
The eligibility criteria for research study participants included (a) being at least 18
years’ old, (b) living in the United States, (c) working as a technology sales professional,
and (d) receiving a sales performance evaluation for the previous fiscal year. Voluntary
participation in this study included competent technology sales professionals’
knowledgeable about the purpose of the study. According to Kjervik’s (2009) definition
of vulnerable populations, my study did not knowingly include any class of protected
people. Although pregnant women may have completed the survey and assessment, none
of the survey questions collected this information, removing any potential for bias.
48
Two United States-based technology sales population groups were sampled. My
company’s sales team, and my LinkedIn sales contacts participated. My company
employment provided access to study participants. The company leadership approved
my request to survey and assess their technology sales professionals (see Appendix A).
Once I received approval to conduct research from Walden University’s Institutional
Review Board (see Appendix C), I contacted potential participants using the company
email system. My company’s Human Capital Group assigned a Resource Representative
for me to coordinate with before, during, and after the assessment.
Several authors recommend establishing communication with the desired
population before assessment when using an online survey and assessment to collect data,
so as to improve the acceptable participant response rate (Chang, 2013; Puleston, 2011).
Because I was mentioned in the recruitment materials, my employment may have served
as a motivator to participate for some technology sales representatives at my company.
My familiarity with LinkedIn participants did motivate participants, because it offered an
opportunity to identify ways for participants to improve themselves and their company.
The technology sales professionals from my company and LinkedIn collectively
made up a diverse group. Using this group as my research sample provided evidence of
significant correlation between emotional intelligence and sales performance.
Participants were permitted to receive results of their individual emotional intelligence
assessment. My company established an email alias for participants to submit requests. I
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used company provided de-identified email to send individual report results directly to
the participants. LinkedIn participants contacted me directly for results.
Research Method and Design
I conducted a quantitative correlational research study to examine the relationship
between emotional intelligence and sales performance of United States-based sales
professionals. The independent variables were (a) emotional intelligence, (b) self-
perception, (c) self-expression, (d) interpersonal, (e) decision making, and (f) stress
management. The dependent variable was the sales performance of United States-based
technology sales professionals.
Research Method
I selected a quantitative method as the best fit for assessing the relationship
between emotional intelligence and sales performance of United States-based sales
professionals, and resources available to complete the study. Quantitative research relies
primarily on collecting and analyzing numerical data (Bansel & Corley, 2012; Cooper &
Schindler, 2011), and are frequently used to determine relationships between variables
(Bernard, 2013). Mengshoel (2012) suggested using a quantitative method when
research requires the generation of variables to prove a hypothesis. The decision to use a
quantitative method came from the need to evaluate the relationship between emotional
intelligence and sales performance of United States-based sales professionals, and
resources available to complete the study in a timely manner.
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I did not select a qualitative or mixed methods study. Using a qualitative method
would produce a rich description of the experiences (Erlingsson & Brysiewicz, 2013;
Schleifer & Rothman, 2012) of United States-based technology sales professionals. Such
a method would not permit testing whether emotional intelligence varied with sales
performance. Mengshoel (2012) suggested the use of a mixed methods approach when
combining a qualitative and quantitative method to enhance research outcomes.
Conducting a mixed methods study would yield both comprehensive investigation of the
situation or circumstance and theory testing (Bernard, 2013; Freshwater, 2014; Teddlie &
Tashakkori, 2011; Zohrabi, 2013). To complete such a study would require more
resources than available to me. Given business leaders have a limited understanding of
the relationship between emotional intelligence and sales performance, using quantitative
techniques was the best option available to test for a relationship between variables with
limited resources.
Research Design
I considered experimental, descriptive, and correlational designs before selecting
a correlational research design for this study. A quantitative study that explores
relationships between variables must use a correlational design (McCusker, & Gunaydin,
2014; Pilcher & Bedford, 2011; Trusty, 2011). With experimental research, the focus is
on determining if a particular treatment will influence an outcome by manipulating
variables (Bansel & Corley, 2012; Larwin & Larwin, 2011). I eliminated an
experimental design from consideration because this research was not intended to
51
manipulate either variable, but to discover if a relationship exists between the variables of
emotional intelligence and annual sales performance. While descriptive designs examine
the current condition of a situation or circumstance (Bernard, 2013; Borbasi & Jackson,
2012; Ingham-Broomfield, 2014; Revicki, & Schwartz, 2014), I instead examined the
relationship among variables using numerical data. Because my research did not
manipulate any variables, or describe the current state, a correlational design was the
most appropriate strategy of inquiry for measuring the relationship between variables.
Population and Sampling
Two United States-based technology sales population groups were sampled. My
company’s sales team, and my LinkedIn sales contacts participated. This group of sales
professionals aligned to the study research question by representing a blend of human
characteristics while working in a sales environment requiring emotional intelligence. To
conduct this study, I used a nonprobability purposeful sample of United States-based
technology sales professionals working at my company and through my LinkedIn sales
contacts. This was an appropriate technique based on the research question, quantitative
method, and correlational design of this study. Purposive sampling is the recruitment of
study participants based on certain criteria (Bernard, 2013). Suri (2011) defined
purposeful sampling as a means to identify study participants who may provide an in-
depth understanding of the research phenomenon. Purposive sampling is an inexpensive
and practical method but restricts a researcher’s ability to generalize results (Bernard,
2013).
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Because purposive sampling is a nonprobability sampling method, there was a
need for adequate sample sizing to alleviate validity and generalization concerns
(Noordzij, Dekker, Zoccali, & Jager, 2011; Patterson & Morin, 2012). Determining the
correct sample size requires finding appropriate values for statistical power, alpha, and
effect size. Using G*Power 3.1.9 software, a sample size power analysis was conducted.
G*Power 3.1 is open-source software created by the faculty at the Institute for
Experimental Psychology in Dusseldorf, Germany (Faul, Erdfelder, Buchner, & Lang,
2009).
Statistical power is “the probability of rejecting the null hypothesis and speaks to
the likelihood of confirming the alternative hypothesis or research hypothesis” (Liu,
2012, p. 427). High statistical power helps establish credibility regarding the research
hypothesis (Liu, 2012). The accepted value for statistical power is 80% (Bernard, 2013).
In psychological research, the standard alpha level (α) is .05, which means there would be
a 95% chance the correct conclusion was reached (Noordzij et al., 2011). A trade-off
exists between Type I and Type II errors. Type 1 error rejects the null hypothesis when it
is true. Type II error rejects an alternative hypothesis when it is true. To preserve
credibility, it is important that the sensitivity of the test be set so it would detect any real
relationships by rejecting the null hypothesis when it is false (Noordzij et al., 2011).
Effect size is a quantitative consideration of phenomenon magnitude (Kelley & Preacher,
2012). Prior studies of emotional intelligence and job performance found a medium
effect size (Griffin, 2013).
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An apriori power analysis was conducted. Assuming a medium effect size (f² =
.20) and standard alpha level (α = .05), with six predictor variables, results indicated a
minimum sample size of 75 participants required to achieve a power of .80. Increasing
the sample size to 111 would increase power to .95. I sought between 75 and 111
participants for the study (Figure 2).
Figure 2. Power as a function of sample size.
With a well thought through plan, “online survey data can be equal or superior to
that of equivalent paper survey data” (Chang, 2013, p. 121). With 329 eligible
participants, 210 through LinkedIn and 119 through my company, achieving the required
sample of 75 respondents necessitated a response rate of 23%. Online surveys have an
average response rate between 24% - 30% (Sanchez-Fernandez, Munoz-Leiva, &
Montoro-Rios, 2012). Study participation exceeded the required sample size.
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Ethical Research
Electronic communication was sent to technology sales representatives before,
during, and after the emotional intelligence assessment. Electronic communication sent
prior to the assessment period explained the voluntary nature of the study, its purpose and
intended use, and confidentiality and privacy (see Appendix D). Guidelines established
by Walden University’s Institutional Review Board were followed. Completion of the
study’s survey and assessment instruments by participants was voluntary.
Participants had the right to withdraw before, during, and after the assessment.
No participants withdrew during or after the assessment. Informed consent and invitation
emails were sent to participants (see Appendix E). Within the invitation email,
participants were notified that they had the right to withdrawal from the study up to 45
days. Once inside MHS’ web portal, participants were asked to read and agree to “click
to” informed consent. By not clicking and agreeing to continue, participants were unable
to take the assessment whereby withdrawing from the study.
No incentives were offered for participating. Respondent results were kept in
strict confidence by me. Although MHS retained the right to use and publish
nonidentifiable data, they also acknowledge that they protect any transmitted personal
information. Related researcher guidelines that protect participants and ensure an ethical
study were followed per Murray (2014). All information related to participants’ results
were downloaded to a password-protected storage device and are stored in a locked file
55
for five years, at which point all associated data will be destroyed. Walden University
IRB approval number is 05-21-15-0244380 (see Appendix C).
Data Collection Instruments
Demographic Survey
The eligibility criteria for research study participants included (a) being at least 18
years’ old, (b) living in the United States, (c) working as a technology sales professional,
and (d) receiving a sales performance evaluation for the previous fiscal year. Eligible
participants received an informed consent and invitation email (see Appendix E). Once
inside MHS’ web portal, participants were asked to read and agree to “click to” informed
consent. By not clicking to continue, participants were unable to take the assessment
whereby withdrawing from the study. Participants were then asked to provide either their
full name or reference identification. Next, four optional demographic questions were
asked. Questions included age, gender, occupation group, and occupation code. MHS
uses this data to normalize the dataset. Collecting demographic information is a routine
survey method; the information can be used for both descriptive and statistical analyses
(Bernard, 2013). Once this optional demographic survey was completed, participants
were provided instructions on how to take the survey. By not clicking to continue,
participants were unable to take the emotional intelligence assessment whereby
withdrawing from the study.
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EQ-i 2.0® Assessment
I selected the online version of the EQ-i 2.0® (Multi-Health Systems, Inc., 2011).
Available for use since 2011, permission was granted by MHS to administer and score
participant results (see Appendix B). The EQ-i 2.0® was designed as a norm-referenced
forced-choice instrument to measure social skills (Multi-Health Systems, Inc., 2011).
These skills impact the way individuals develop and maintain relationships and cope with
stressful situations (Multi-Health Systems, Inc., 2011). The EQ-i 2.0® can be
administered to participants in a variety of occupational settings (Multi-Health Systems,
Inc., 2011).
The EQ-i 2.0® provided an emotional intelligence score, based on the Self-
Perception, Self- Expression, Interpersonal, Decision Making, and Stress Management
composite scale scores (Multi-Health Systems, Inc., 2011). The Self-Perception
composite scale score included emotional self-awareness, self-regard, and self-
actualization subscales (Multi-Health Systems, Inc., 2011). The Self-Expression
composite score included emotional expression, assertiveness, and independence
subscales (Multi-Health Systems, Inc., 2011). The Interpersonal composite score is
based on interpersonal relationships, empathy, and social responsibility subscales (Multi-
Health Systems, Inc., 2011). The Decision Making composite score is calculated using
the scores from impulse control, problem-solving, and reality testing subscales (Multi-
Health Systems, Inc., 2011). The Stress Management composite score is based on
57
flexibility, stress tolerance, and optimism subscale scores (Multi-Health Systems, Inc.,
2011).
By selecting the online version of the EQ-i 2.0® emotional intelligence
assessment, I evaluated the research question regarding the relationship between
emotional intelligence and sales performance. Although participants were geographically
dispersed, they all had access to a computer and Internet connectivity. Eligible
participants received an informed consent and invitation email (see Appendix E), which
contained a link to MHS secure online portal. Once accessed, participants were asked
again to read and agree to “click to” informed consent. By not clicking to continue,
participants were unable to take the survey and assessment whereby withdrawing from
the study. Assessment took no more than 30 minutes to complete.
The EQ-i 2.0® includes a 133-item emotional intelligence model that uses a five-
point Likert ordinal scale with responses that ranges from (1) “never/rarely" to (5)
“always/almost always” (Multi-Health Systems, Inc., 2011). The EQ-i 2.0® provides an
emotional intelligence score, based on the Self-Perception, Self- Expression,
Interpersonal, Decision Making, and Stress Management branch scale scores (Multi-
Health Systems, Inc., 2011). A raw score was calculated and compared to the mean and
standard deviation for the particular branch scale (Multi-Health Systems, Inc., 2011).
MHS does not publish the mean and standard deviation for each branch scale. MHS does
provide formula: Standard Score = (raw score – M)/SD x 15 + 100. Employing a 1-5-15
factor structure, the EQ-i 2.0® features one total emotional intelligence score, five
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composite scores, and 15 subscale scores (Multi-Health Systems, Inc., 2011). For the
purposes of this study, only emotional intelligence and composite scale scores will be
used because of their preferred psychometric properties.
Scoring used to interpret results of this study was consistent with the methods
employed by the EQ-i 2.0® Workplace Report (Multi-Health Systems, Inc., 2012). MHS
reports that a score of 70 to 90 is considered in the low range of emotional intelligence
and indicates an opportunity for personal development. MHS reports that a score of 91 to
110 is a mid-range score. Any score above 110 indicates a high score (Multi-Health
Systems, Inc., 2012); individuals with scores in this range demonstrated good emotional
intelligence. Any score below 100 is a potential target area for development (Multi-
Health Systems, Inc., 2012).
The instrument was developed using large heterogeneous samples. MHS reports
that the EQ-i 2.0® normative sample includes ten age ranges (400 cases in each age
range), equally proportioned by gender. MHS reports that the normative sample is
similar to the Censuses (within 3%) regarding race/ethnicity, geographic region, and
education level. The United States/Canada Professional normative group was used for
scoring purposes. Participants’ scores were compared with other North American
professionals, versus the general population. It was possible to use this particular
normative group since eligible participants included highly educated technology sales
professionals.
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Within the past five years, researchers studying the relationship between
emotional intelligence and sales performance have used Bar-On’s EQ-i as their preferred
emotional intelligence measurement. For example, Enhelder (2011) investigated the
relationship between emotional intelligence and sales performance among 717 financial
advisors. Results demonstrated a statistical relationship between emotional intelligence
and financial advisor sales performance (Enhelder, 2011). Farnham (2012) studied the
relationship between sales performance and emotional intelligence of 35 hospice sales
professionals. Results indicated a relationship between emotional intelligence and
hospice sales performance (Farnham, 2012). Harris et al. (2012) assessed the relationship
between emotional intelligence and sales performance among 136 medical sales
representatives. Results indicated differences in emotional intelligence scores by gender
and tenure, but showed no statistical relevance between emotional intelligence and sales
performance.
The EQ-i 2.0® is a valid and reliable measurement of emotional intelligence
(Multi-Health Systems, Inc., 2011). For multiple-item scales, the most frequently
reported reliability statistic is Cronbach’s coefficient alpha (Eisinga, Grotenhuis, &
Pelzer, 2012). The alpha value of the Emotional Intelligence Score is 0.97, with
composite scales ranging from 0.88 to 0.93 (Multi-Health Systems, Inc., 2011). Values
above 0.7 are considered acceptable (Tavakol & Dennick, 2011). A reported 90%
confidence interval is used for the EQ-i 2.0® scores (Multi-Health Systems, Inc., 2011).
Indicating the EQ-i 2.0® is a stable assessment of emotional intelligence, test-retest
60
correlations ranged from (r = 0. 92) for 2 to 4 week values, and from (r = 0.81) for 8
week values (Multi-Health Systems, Inc., 2011).
Content validity demonstrates all aspects of the emotional-social inventory
construct are captured by the EQ-i 2.0® (Multi-Health Systems, Inc., 2011). MHS reports
that the EQ-i 2.0® is correlated with the original EQ-i but does not correlate with
measures of ability-based emotional intelligence assessment such as MSCEIT. No racial
or ethnic bias was found in the EQ-i 2.0® (Multi- Health Systems, Inc., 2011). The factor
structure was validated using exploratory and confirmatory factor analyses (Multi-Health
Systems, Inc., 2011).
For this research study, no modifications were made to the EQ-i 2.0® survey
instrument. Permission to use EQ-i 2.0® was received by MHS (see Appendix B).
Scored study group results were downloaded by me. The EQ-i 2.0® survey instrument
was chosen based on its psychometric properties of reliability and validity, past use
measuring the relationship between sales performance and emotional intelligence, and
ease of administering online to a geographically dispersed target population.
Data Collection Technique
The data collection techniques used included an online survey and emotional
intelligence assessment, and company provided and LinkedIn self-reported sales
performance data. Accessed through an electronically mailed website link, online survey
and assessment site were securely hosted by MHS whom I contracted with to administer,
collect, and analyze results. According to Cooper and Schindler (2011), the convenience
61
of electronic communication makes research topics more accessible because of the ease
of instrument dissemination and data gathering. Surveys are also a cost-effective way of
collecting data (O’Rourke, 2011). Sanchez-Fernandez et al. (2012) stated that online
surveys have an average response rate between 24% - 30%. Web-based surveys can
produce lower response rates than mail surveys (Sauermann & Roach, 2013).
Through intercompany email, my company’s Human Capital Group provided me
with a Microsoft Excel spreadsheet containing its eligible de-identified technology sales
professionals’ contact and sales performance data. Using my LinkedIn account, I had
connections to 210 United States-based technology sales professionals. LinkedIn
participants emailed me their self-reported sales performance. Electronic information
collected was securely hosted on my password protected home office computer. The
eligibility criteria for research study participants included (a) being at least 18 years, (b)
living in the United States, (c) working as a technology sales professional, and (d)
receiving a sales performance evaluation for the previous fiscal year. Specific
information provided by my company’s Human Capital Group included (a) an
anonymous employee reference identification number, (b) de-identified, anonymous
email address, (c) job classification title, and (d) the individual’s percent attainment to in
year revenue target for fiscal year ending March 31, 2015. For job classification, client
management was defined as owners of account profit and loss while sales management
was defined as owners of new account acquisition.
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Permission was obtained from MHS to administer the EQ-i 2.0® assessment
through its secure website (see Appendix B). MHS hosted results in a password
protected web portal and provided scored results upon my request. Using administrator
login credentials supplied by MHS, I created a secure research project site. Using de-
identified email addresses provided to me by my company, informed consent and
invitations were sent to individual participants from this site (see Appendix E). Informed
consent and invitations were also sent to LinkedIn participants from this site. Invitations
included a unique reference identification number and secure website address for
participants to select. Once selected, respondents were sent to the secure research
website to take the optional demographic survey and emotional intelligence assessment.
Upon completion of the two week assessment, results were downloaded by me to
my home office computer. Specific post assessment information provided by MHS but
not limited to included (a) employee reference identification number, (b) age, (c) gender,
(d) emotional intelligence score, (e) self-perception composite scale score, (f) self-
expression composite scale score, (g) interpersonal composite scale score, (h) decision
making composite scale score, and (i) stress management composite scale score.
Company participant data was de-identified to protect the security and confidentiality of
respondents. All research files are kept on a password protected home computer. This
computer is in my home office and not accessible to the public. Participants’ results were
archived to a password protected storage device and stored in a locked cabinet. After five
years, I will destroy all associated project data.
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Data Analysis
The overarching research question for this study was: What is the relationship
among emotional intelligence, self-perception, self-expression, interpersonal, decision
making, stress management, and sales performance?
The six hypotheses proposed for this study included:
H10: There is no statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H1a: There is a statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H20: There is no statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H2a: There is a statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H30: There is no statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
H3a: There is a statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
H40: There is no statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
H4a: There is a statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
64
H50: There is no statistical relationship between a United States-based technology
sales professional’s decision making composite score and sales performance.
H5a: There is a statistical relationship between a United States-based technology
sales professional’s decision making composite score and sales performance.
H60: There is no statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
H6a: There is a statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
I used Statistical Packages for the Social Sciences (SPSS) version 21, a
proprietary software produced by IBM©, for data analysis. By merging my company’s
technology sales professionals’ eligibility dataset (stored in a Microsoft Excel
spreadsheet) with MHS survey and assessment results (also stored in a Microsoft Excel
spreadsheet), I created a company master file. By merging LinkedIn’s technology sales
professionals’ self-reported dataset with MHS survey and assessment results, I created a
LinkedIn master file. Combining both master files created a study master file, which was
used to conduct data analysis. For the purposes of this study, SPSS accounted for
missing data, generated descriptive statistics, created histograms and scatterplots, and
conducted correlational and multiple regression analyses.
Descriptive statistics were conducted on both categorical and continuous
variables. Categorical variables included job classification title and gender. Frequency
and percentages were conducted for categorical variables. Continuous variables include
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(a) percent attainment to previous year’s revenue target, (b) age, (c) emotional
intelligence score, (d) self-perception composite scale score, (e) self- expression
composite scale score, (f) interpersonal composite scale score, (g) decision making
composite scale score, and (h) stress management composite scale score. Minimum and
maximum scores, mean, standard deviation and Cronbach alpha reliability were
conducted on the continuous variables. Cronbach alpha values above 0.70 demonstrate
acceptable internal consistency reliability for the sample scale (Cho & Kim, 2015).
Answers to the demographic survey and emotional intelligence assessment
produced ordinal and nominal, nondichotomous, data. This type of data is suitable for
testing by means of inferential statistics (Nayak & Hazra, 2011). Inferential statistics use
both normally distributed parametric (e.g., t-test, ANOVA, Pearson r correlation) and
nonparametric techniques (e.g., Spearman rho, Kruskal-Wallis, Mann-Whitney U, Chi-
square). Examination of the research variables from one sample population without
manipulation, indicate that the most appropriate statistical tests to understand the
relationship between emotional intelligence scores (independent variable) and sales
performance (dependent variable) were correlation and regression analyses.
Prior to performing a correlation analysis to assess hypotheses 1-6, a scatterplot
analysis was conducted to ensure assumptions of linearity and homoscedasticity were
met. If nonlinear distributed data was confirmed, a Spearman rho correlation test would
be used. Otherwise, a Pearson correlation test was conducted. Variables include (a) job
classification title, (b) percent attainment to in year revenue target for previous fiscal
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year, (c) age, (d) gender, (e) self-perception composite scale score, (f) self-expression
composite scale score, (g) interpersonal composite scale score, (h) decision making
composite scale score, (i) stress management composite scale score, and (j) emotional
intelligence score.
The Pearson correlation coefficient calculation returns a value between -1 and +1,
with “0” denoting no relationship at all (Prion & Haerling, 2014). The higher the
absolute value of the number, the stronger the relationship between the two variables
(Lind, Marchal, & Wathen, 2012). Within this study, the closer the Pearson (r) gets to
zero, the more likely the null hypothesis will not be rejected. The further the Pearson (r)
moves away from zero and closer to absolute 1, the more significant the relationship
between the variables and the more likely the null hypothesis will be rejected. The
relationship described by a correlation coefficient does not imply causality between the
two variables. The level of statistical significance, p-value, indicates how much
confidence is placed on the results accuracy. According to Lind et al. (2012), the p-value
is the probability, based on the observation of the sample, which the null hypothesis is
rejected. Evidence that would support a rejection of the null as significant would be a p-
value less than 0.05.
To assess predictability of independent variables over the dependent variables,
standard multiple regression analysis was used. Independent continuous predictor
variables included (a) emotional intelligence score, (b) self-perception composite scale
score, (c) self- expression composite scale score, (d) interpersonal composite scale score,
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(e) decision making composite scale score, and (f) stress management composite scale
score. Dependent categorical variables included the individual’s percent attainment to in
year revenue target for previous fiscal year. Sample size, multicollinearity, and outliers
were checked to ensure appropriate use of multiple regression analysis. To test for high
intercorrelations among predictor (independent) variables, I ran collinearity diagnostics.
Missing data can weaken the representativeness of the sample, which may
negatively affect the reliability of the results, such as biasing inferences (Fleming, 2011).
To counter this possibility, weekly meetings with my company’s Human Capital Group
were scheduled before, during, and after the assessment. My company provided
complete data. Missing EQ-i 2.0® data was a concern because it could reduce the test’s
validity. Although MHS permits participants to proceed when questions are skipped on
its survey or EQ-i 2.0® assessment, emotional intelligence scores and branch scores were
not generated if missing data reached 8% (Multi-Health Systems, 2011). If participants
exited the EQ-i 2.0® and did not return before the assessment taking window closed, no
scores were generated for that participant.
Study Validity
A research study’s reliability and validity are dependent on the instruments and
processes adopted. Best practices exist for protecting and enhancing a study’s reliability
and validity. Consideration and planning to address challenges with reliability and
validity are best addressed during a study’s design phase (Bernard, 2013).
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Research study reliability mirrors the consistency of a study and its
instrumentation. Researchers should verify instruments for reliability (Bhattacherjee,
2012). As discussed in the preceding section on data collection instruments, MHS
reports that the EQ-i 2.0® is a valid and reliable measurement of emotional intelligence.
Cronbach’s coefficient alpha value of the Emotional Intelligence Score is 0.97, with
composite scales ranging from 0.88 to 0.93 (Multi-Health Systems, Inc., 2011). Values
above 0.7 are considered acceptable (Tavakol & Dennick, 2011). To ensure instrument
reliability on my study sample, I used SPSS to compute Cronbach’s alpha and reported
the results in Section 3, “Presentation of Findings.” By presenting participants with clear
instructions on how to complete the survey and assessment, collecting reliable data from
respondents is increased (Fan & Yan, 2010). The risk of researcher error diminishes and
study reliability increases because of such controls (Barends, Janssen, ten Have, & ten
Have, 2013; Fan & Yan, 2010).
Through study design, an optimal balance between internal and external validity
can exist (Bhattacherjee, 2012). Cantrell (2011) warned that improving a study’s internal
validity could diminish its external validity. Studies attempting to prove a relationship
between cause and effect that do not employ random sampling are susceptible to internal
validity concerns (Bernard, 2013). This study included a nonprobabilistic purposeful
sample of United States-based technology sales professionals working at my company
and through my LinkedIn connections. Including eligibility criteria for study
participation can improve internal validity (Cantrell, 2011).
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According to Bernard (2013), to improve external study validity, researchers
should consider increasing the sample size, selecting a population representative of the
general population, and conducting a multi-year study. To increase my sample size,
technology sales professionals employed by my company received an awareness email
prior to an invitation to participate in this study. This sample group of sales professionals
aligned to the study research question by representing a blend of human characteristics
while working in a sales environment requiring emotional intelligence. Given this was
not a long-term study, this threat to external validity remains.
If the study theories selected relate to the participants selected, construct validity
in a nonexperimental study can be achieved (Stone-Romero, 2010). By deciding to study
if a relationship exists between emotional intelligence and sales performance, I tested
emotional intelligence theory. The EQ-i 2.0® instrument reported high construct validity
through common factor analysis (Multi-Health Systems, Inc., 2011). The literature
supports the use of this instrument when studying emotional intelligence and sales
performance (Enhelder, 2011; Farnham, 2012; Megowan, 2012), and ensures construct
validity (Bernard, 2013). Construct validity requires reasonable measurement of the
construct (Hair, Celsi, Money, Samouel, & Page, 2011). Internal validity involves
credibility of the variable development and developing causal and logical deductions.
(Bleijenbergh, Korzilius, & Vershuren, 2011).
Statistical validity depends on a sufficient sample size, using the right statistical
tests to analyze collected data, using the correct level of statistical power, and
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determining the correct Type I error rate (Barends et al., 2013). For this study, an apriori
power analysis was conducted using G*Power 3.1.9 software to calculate sufficient
sample size. By selecting Pearson’s correlation coefficient (r) and linear multiple
regression as the statistical tests, the statistical validity of this study was improved.
Transition and Summary
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. Section 2 outlined my role as researcher, listed eligibility criteria for
participants, and described reasoning behind selection of a quantitative correlational
study design. Two United States-based technology sales population groups were
sampled. Required sample size was calculated to be 75. Consideration and compliance
to ethical standards were reviewed. Data collection instruments and techniques were
described as were data analyses proposed methods. Section 2 concluded with a
discussion on study reliability and validity. Section 3 includes presentation of the
findings, a discussion regarding the applicability to professional practice, the implications
for social change, recommendations for action and further research, reflections, and the
conclusion of the study.
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Section 3: Application to Professional Practice and Implications for Change
This section provides a brief overview of the study and a presentation of the
findings. Discussion of how applicable these results are to professional practice, and an
exploration of how these findings can influence technology sales professionals’ wellbeing
and the
customers they serve is explored. This section also includes recommendations for
action and opportunities for further study, and concludes with my reflections and closing
remarks.
Overview of the Study
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. This study used inferential statistics (Pearson’s coefficient and
multiple linear regression analysis) to test for the existence of a
relationship between the
variable scores of emotional intelligence, self-perception, self-expression, interpersonal,
decision making, stress management, and sales performance. Following best procedures
for ensuring statistically valid results (Bernard, 2013), the p-value for these tests was set
to 0.05. Results demonstrated a significant association measured between decision
making and sales performance (r = .310, p ˂ .01). No associations existed between
emotional intelligence and sales performance (r = .229, p = ns); self-perception and sales
performance (r = -.157, p = ns); self-expression and sales performance (r = .212, p = ns);
interpersonal and sales performance (r = .094, p = ns); and stress management and sales
performance (r = .225, p = ns).
72
For all six predictor variables, the regression model was not a significant predictor
of sales performance, F(6,66) = 1.295, p = .272, R² = .105. By including only decision
making, the linear regression model was a significant predictor of sales performance,
F(1,71) = 7.550, p ˂ .01, R² = .096. The conclusion from this analysis is that decision
making holds significance in achieving sales performance.
Presentation of the Findings
The overarching research question for this study was: What is the relationship
among emotional intelligence, self-perception, self-expression, interpersonal, decision-
making, stress management, and sales performance?
The six hypotheses for this study were:
H10: There is no statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H1a: There is a statistical relationship between a United States-based technology
sales professional’s emotional intelligence score and sales performance.
H20: There is no statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H2a: There is a statistical relationship between a United States-based technology
sales professional’s self-perception composite score and sales performance.
H30: There is no statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
73
H3a: There is a statistical relationship between a United States-based technology
sales professional’s self-expression composite score and sales performance.
H40: There is no statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
H4a: There is a statistical relationship between a United States-based technology
sales professional’s interpersonal composite score and sales performance.
H50: There is no statistical relationship between a United States-based technology
sales professional’s decision making composite score and sales performance.
H5a: There is a statistical relationship between a United States-based technology
sales professional’s decision making composite score and sales performance.
H60: There is no statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
H6a: There is a statistical relationship between a United States-based technology
sales professional’s stress management composite score and sales performance.
Prior to the beginning of data collection, my company lowered the number of
eligible technology sales professional participants to 119. The loss of eligible
participants was attributed to year-end attrition. Because this change would require an
unlikely 63% survey response rate to achieve the desired sample size of 75 participants, I
recruited a second sample group using my LinkedIn contacts. Since 2004, I have stayed
in contact with 210 United States-based technology sales professionals through LinkedIn.
Informed consent and invitation emails were sent to all eligible LinkedIn contacts
74
requesting participation and explaining eligibility criteria. Over a 2-week assessment
period, participants completed the survey and sent me self-reported sales performance
data through email.
In total, I sent invitations to 329 eligible participants: 210 through LinkedIn and
119 through my company. From LinkedIn, 51 participants completed the survey, with 44
of them also supplying self-reported sales performance data. From my company, 42
participants completed the survey with my company supplying sales performance data on
all. The
response rate for this research study was 26%, 86 completed out of 329
invitations. This response rate exceeded the minimum
sample needed for statistically
valid results, which was established at 75 respondents per G*Power 3.1. The returned
sample size of 86 was large enough to support
the study with a confidence level of 95%
and a statistical power of .80, per the best procedures described by Bernard (2013) and
Sanchez et al. (2012).
Descriptive Statistics
Table 1 shows the demographic details for the participants of this study. Age and
gender data were captured during the optional online demographic survey. For job
classification, my company provided that description with participant sales performance
data. LinkedIn participants were asked to self-categorize when reporting their sales
performance data. Among combined sample of respondents, nine in ten were male
(92.5%). LinkedIn (93.2%) and my company (91.7%) had similar male participation
rates. Study participants’ gender breakdown was similar to gender patterns found in other
75
studies examining emotional intelligence and sales performance (Griffin, 2013;
Haakonstad, 2011). Comparisons to similar studies provide context and assistance in
understanding this study’s results.
The combined sample revealed that those in their forties represented four in 10
(42.7%) of the respondents, followed by those in their fifties (29.3%) and those in their
thirties (20.0%). While the LinkedIn sample resembled the combined aged outcomes, the
company sample varied. Leading with respondents in their forties, the company sample
followed with those in their thirties (29.4%), and those in their fifties (26.5%).
Distribution of study participants’ age mirrored those of other studies examining
emotional intelligence and sales performance (Griffin, 2013; Megowan, 2012).
For role, client management was defined as owners of account profit and loss
while sales management was defined as owners of new account acquisition. Sales
management made up slightly more than half of all participants (54.7%). While the
LinkedIn sample resembled the combined role outcomes (61.4%), the company sample
had slightly more client management professionals respond (52.4%). This role similarity
extended
to studies examining emotional intelligence and sales performance (De La Cruz et
al., 2014; Farnham, 2012; Gahan, 2012; Griffin, 2013). The demographic data shown in
Table 1 revealed respondents were primarily male, middle-aged, and served in sales
management.
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Table 1
Demographic Description of Sample
The second portion of the online survey, the EQ-i 2.0® (Multi-Health Systems,
Inc., 2011), provided data on technology sales professionals’ emotional intelligence.
Tables 2 and 3 report participants’ emotional intelligence scores and sales performance
expressed as percent attainment of previous years’ revenue objective. Combined
emotional intelligence scores had a median of 108.00 and a standard deviation of 12.15,
indicating participants scored on the higher end of the average range. LinkedIn sample
median was higher (111.00) than the company (106.00). Combined sales performance
Variable nValid % nValid % nValid %
Gender
Male 74 92.5 41 93.2 33 91.7
Female 6 7.5 36.8 38.3
Missing 6 0 6
Age 31-39 15 20.0 512.1 10 29.4
40-49 32 42.7 20 48.9 12 35.3
50-59 22 29.3 13 31.7 926.5
60+ 68.00 37.3 38.8
Missing 11 3 8
Role Client Mangement 39 45.3 17 38.6 22 52.4
Sales Management 47 54.7 27 61.4 20 47.6
Missing 0 0 0
Note. N = 86.
Combined
LinkedIn
Company
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achievement had a median of 0.86 (86%) and a standard deviation of 0.61. LinkedIn
sample self-reported median was higher (0.90) than the company (0.77). In this study,
the distribution of emotional intelligence and sales performance scores mirrored those of
other studies (Griffin, 2013).
Table 2
Descriptive Statistics for Population Samples
Variable Min Max Mdn SD Min Max Mdn SD
Emotional Intelligence 61.00 128.00 111.00 13.460 81.00 122.00 106.00 10.493
Self-Perception 70.00 123.00 110.00 12.958 69.00 118.00 105.00 10.001
Self-Expression 69.00 129.00 109.50 12.946 74.00 122.00 102.50 13.017
Interpersonal 68.00 126.00 109.00 14.055 81.00 121.00 104.50 10.426
Decision Making 68.00 125.00 107.00 13.029 75.00 126.00 107.00 11.894
Stress Management 59.00 128.00 111.00 12.659 75.00 124.00 104.50 9.952
Sales Performance 0.00 3.40 0.90 0.502 0.00 4.12 0.77 0.698
Note. N = 86.
LinkedIn
Company
78
Table 3
Descriptive Statistics for Continuous Variables
To prepare for an accurate analysis of parametric statistical methods, normal
distribution of the data under analysis must be required (Hair et al., 2011; Siddiqi, 2014).
For all continuous variables, histograms, normality plots, skewness, kurtosis, and Shapiro-
Wilk tests were conducted. To bring variables into normality, scores from extreme
outliers were removed from sales performance, self-perception, self-expression, decision
making, and stress management. Data transformation was also performed on the self-
Variable Min Max Mdn SD
Emotional Intelligence 61.00 128.00 108.00 12.149
Self-Perception 69.00 123.00 107.50 11.570
Self-Expression 69.00 129.00 106.00 13.211
Interpersonal 68.00 126.00 105.50 12.464
Decision Making 68.00 126.00 107.00 12.417
Stress Management 59.00 128.00 107.00 11.471
Sales Performance 0.00 4.12 0.86 0.605
Note. N = 86.
Combined
79
perception variable. Figures 3 and 4 show histograms for emotional intelligence and sales
performance scores.
Figure 3. Histogram showing the distribution of Emotional Intelligence scores.
80
Figure 4. Histogram showing the distribution of sales performance scores.
Skewness is the amount and direction of the curve. Kurtosis is an indication of the
height and
sharpness of the central peak relative to the shape of a normal curve. Values
for both Skewness and Kurtosis should be as close to zero as possible. Skewness and
Kurtosis z-values of ±1.96 were acceptable for this study. Another test of data normality,
Shapiro-Wilk, was conducted that confirmed normality of data under study. As shown in
Table 4, continuous variables were normally distributed.
81
Table 4
Normality of Continuous Variables
An accurate analysis of inferential statistics requires that assumptions of
multicollinearity, linearity, and homoscedasticity are met (Pallant, 2010).
Multicollinearity can negatively impact multiple regression analysis if a high degree of
correlation between independent variables exists (Pallant, 2010). Cronbach’s α is a
respected method of estimating reliability (Geldhof, Preacher, & Zephyr, 2013). As
reported by MHS, the EQ-i 2.0® scale has good internal consistency, with a Cronbach
alpha coefficient of 0.97. In the current study, the Cronbach alpha coefficient was 0.94
Statistic SE zStatistic SE z
Emotional Intelligence -.510 .269 -1.896 -.152 .532 -.286
Self-Perception .010 .263 .039 -.268 .520 -.516
Self-Expression -.489 .261 -1.871 -.116 .517 -.224
Interpersonal -.474 .260 -1.824 -.187 .514 -.364
Decision Making -.328 .269 -1.219 -.340 .532 -.639
Stress Management -.438 .264 -1.659 .288 .523 .551
Sales Performance -.372 .272 -1.368 .250 .538 .465
Note. N = 86.
Variable
Kurtosis
Skewness
82
showing high reliability among the six independent variables of emotional intelligence
scores. Descriptions of the study variables under investigation affirm the decision to use
parametric methods such as Pearson’s coefficient and multiple linear regression analysis
to test for a relationship between emotional intelligence and sales performance.
Inferential Statistics
Based on the combined samples’ normal distribution, I selected Pearson’s
coefficient to test for the strength and direction of a
relationship between the variables
of emotional intelligence, self-perception, self-expression, interpersonal, decision
making, stress management, and sales performance. To accommodate missing values, I
excluded cases pairwise. The
results of the correlation testing appear in Table 5. An
analysis of correlations between
the independent and dependent variables showed that
there was not a significant relationship between emotional intelligence and sales
performance (r = .229, n = 73; p = .052); self-perception and sales performance (r = -
.157, n = 77; p = .172); self-expression and sales performance (r = .212, n = 78; p =
.062); interpersonal and sales performance (r = .094, n = 78; p = .412); and stress
management and sales performance (r = .225, n = 76; p = .051).
A significant association was measured between
decision making and sales
performance (r = .310, n = 73; p = .008). The positive value of the r coefficient
indicated that decision making and sales performance move in the same direction. The
higher the decision making score, the higher the sales performance. The coefficient of
83
determination was calculated (r² = .096) making the percent of variance associated with
decision making almost 10%.
Table 5
Pearson Correlations Between Measures of Emotional Intelligence and Sales
Performance
A standard multiple regression analysis was conducted to evaluate how well
emotional intelligence scores predicted sales performance. The six predictor variables
were emotional intelligence, self-perception, self-expression, interpersonal, decision
making, and stress management. The criterion variable was sales performance expressed
1 2 3 4 5 6 7
Pearson
Correlation
1
-.822** .825** .782** .724** .772** .229
n80 80 80 80 79 80 73
Pearson
Correlation
1
-.649** -.647** -.570** -.561** -.157
n84 84 84 80 82 77
Pearson
Correlation
1
.567** .547** .561** .212
n85 85 80 83 78
Pearson
Correlation
1
.317** .542** .094
n80 80 83 78
Pearson
Correlation
1
.625** .310**
n80 80 73
Pearson
Correlation
1.225
n83 76
Pearson
Correlation
1
n78
5. Decision-Making
6. Stress Management
7. Sales Performance
Note. **p < 0.01 level (2-tailed).
Variable
1. Emotional Intelligence
2. Self-Perception
3. Self-Expression
4. Interpersonal
84
as percent attainment of previous years’ revenue objective. Analyses to assess the
validity of assumptions regarding multicollinearity, normality, homoscedasticity, outliers,
and linearity were completed. Results indicated no serious violations.
By including all six predictor variables, the regression model was not a significant
predictor of sales performance, F(6,66) = 1.295, p = .272. Study sample multiple
correlation coefficient was .33. Approximately 11% of the variance in sales performance
can be attributed to emotional intelligence and its composite scales. For all six variables,
the predictive equation is:
Predicted Sales Performance = -.007(emotional intelligence) + .009(self-
perception) + .004(self-expression) + .001(interpersonal) + .012(decision making) +
.003(stress management) – .594.
By using just emotional intelligence, the linear regression model was not a
significant predictor of sales performance, F(1,71) = 3.913, p = .052. The sample
multiple correlation coefficient was .23. Approximately 5% of the variance in sales
performance is attributed to emotional intelligence.
By including only self-perception, the linear regression model was not a
significant predictor of sales performance, F(1,75) = 1.902, p = .172. The sample
multiple correlation coefficient was .16. Approximately 3% of the variance in sales
performance can be accounted for by self-perception. By including just self-expression,
the linear regression model was not a significant predictor of sales performance, F(1,76)
= 3.589, p = .062. The sample multiple correlation coefficient was .21. Approximately
85
5% of the variance in sales performance can be accounted for by self-expression. By
including only interpersonal, the linear regression model was not a significant predictor
of sales performance, F(1,76) = .680, p = .412. The sample multiple correlation
coefficient was .94. Approximately 1% of the variance in sales performance is attributed
to interpersonal. By including just stress management, the linear regression model was
not a significant predictor of sales performance, F(1,74) = 3.938, p = .051. The sample
multiple correlation coefficient was .23. Approximately 5% of the variance in sales
performance can be accounted for by stress management.
By using only decision making, the linear regression model was a significant
predictor of sales performance, F(1,71) = 7.550, p = .008. The sample multiple
correlation coefficient was .31. Approximately 10% of the variance in sales performance
can be accounted for by decision making. The 95% confidence interval for the slope,
.003 to .018 does not contain the value of zero. Strength is significantly related to sales
performance. For decision making, the predictive equation is:
Predicted Sales Performance = .010(decision making) -.330.
Analysis Summary
The purpose for this study was to examine the relationship between emotional
intelligence and sales performance of United States-based sales professionals. Pearson’s
coefficient and multiple linear regression analysis were used to test for the existence of a
relationship between the variables of emotional intelligence, self-perception, self-
expression, interpersonal, decision making, stress management, and sales performance.
86
Assumptions surrounding multiple regression were tested. Results indicated no
assumption violations. The correlation results showed there was an association between
decision making and sales performance (r = .310, n = 73; p ˂ .01). For all six predictor
variables, the regression model was not a significant predictor of sales performance,
F(6,66) = 1.295, p = .272, R² = .105. By including only decision making, the linear
regression model was a significant predictor of sales performance, F(1,71) = 7.550, p ˂
.01, R² = .096. The conclusion from this analysis is that decision making holds
significance in achieving sales performance.
After analyzing these results, I did not reject this study’s first null hypothesis
(H10: There is no statistical relationship between a United States-based technology sales
professional’s emotional intelligence score and sales performance). I did not reject the
second null hypothesis (H20: There is no statistical relationship between a United States-
based technology sales professional’s self-perception composite score and sales
performance. I did not reject the third null hypothesis (H30: There was no statistical
relationship between a United States-based technology sales professional’s self-
expression composite score and sales performance).
I did not reject the fourth null hypothesis (H40: There is no statistical relationship
between a United States-based technology sales professional’s interpersonal composite
score and sales performance). I did reject the fifth null hypothesis (H50: There is no
statistical relationship between a United States-based technology sales professional’s
decision-making composite score and sales performance). I did not reject the sixth null
87
hypothesis (H60: There was no statistical relationship between a United States-based
technology sales professional’s stress management composite score and sales
performance.)
Results of the research confirmed the proposal made by Nadler (2011) that
emotional intelligence will positively affect success in a professional environment. This
study’s results stand in support of Griffin (2013) who found no significant predicative
qualities between emotional intelligence and sales performance of pharmaceutical sales
managers. Griffin (2013) also found statistical significance within an individual branch
of emotional intelligence (understanding emotions) as measured by the Mayer-Salovey-
Caruso Emotional Intelligence Test (MSCEIT). De La Cruz et al. (2014) found
significant correlation between sales performance and emotional intelligence.
The emotional intelligence theory held that I would expect the independent
variables (emotional intelligence constructs), measured by the EQ-i 2.0® survey
assessment, to influence sales performance outcomes given sales professionals reliance
on emotional intelligence qualities. This theory proved to be correct given the positive
correlation and predictive qualities between a branch score (decision making) of
emotional intelligence and sales performance. The job performance theory held that I
would expect the dependent variable (sales performance) to be unique and not
generalizable to my anticipated single company sales population sample. As evidenced
by the two unique samples, LinkedIn and my company, this also proved to be accurate.
88
LinkedIn self-reported sales performance was 13 basis points higher than the company
reported sales performance.
Applications to Professional Practice
The sales team is an essential element in the business-to-business selling process.
For most companies, the salesperson initiates, develops, and nurtures the customer
relationship (Kumar et al., 2014). Organizations all over the world spend billions every
year in training their sales teams (Little, 2014). Business leaders can overlook the gap
between mediocre and high sales performance because productivity exists in both
instances (Frino & Desiderio, 2013). This performance gap can make a marked
difference to the success of a business and effect the development and income of its
salespeople. By understanding the relationship between emotional intelligence and sales
performance, this gap may help close between mediocre and high performance.
The purpose of this quantitative correlational research study was to examine the
relationship between emotional intelligence and sales performance of United States-based
sales professionals. According to the responses received (N = 86), respondents were
primarily male and middle-aged. Half of respondents had job classifications in client
management, while the other half served in sales management. Technology sales
professionals achieved an average of 86% of their annual sales performance target.
Results were much lower for the company provided sales performance achievement
(77%). The study participants were all United States-based technology sales
professionals who struggled to meet sales performance targets.
89
Findings indicate that emotional intelligence does play a role in sales
performance. As measured by the EQ-i 2.0®, there was a significant correlation
measured between the emotional intelligence branch of decision making and sales
performance. Higher decision-making skill leads to higher sales performance. Decision
making also proved to be a significant predictor of sales performance. Approximately
10% of the variance in sales performance can be accounted for by decision making. To
gain a competitive advantage, and better organizational outcomes, business leaders can
use this information to seek out and hire emotionally intelligent sales professionals.
Company leaders can also train existing sales professionals on emotional intelligence
competencies to improve company-wide sales performance (Fu, 2015).
Implications for Social Change
The results of this study indicated that the emotional intelligence branch of
decision making and sales performance have a significant relationship. This finding is
important because it provides business leaders with demonstrable proof of this
relationship, allowing them to use this knowledge to promote positive social change by
funding programs that promote using emotional intelligence skills in the workplace.
Company leaders should use the information in this study to contribute to positive social
change by developing and implementing sales training and recruitment programs that
promote the well-being of its sales professionals. Effective programs will empower sales
professionals skill sets necessary to achieve personal satisfaction and sales performance
targets.
90
Sales professionals could develop and learn to control their emotions more
effectively when dealing with customers and their companies. Emotionally intelligent
sales professionals may even have a modeling effect on their peers, subordinates, and
leadership. These outcomes could lead to lower salesperson turnover, higher sales
performance, and greater organization effectiveness. Emotional intelligence may become
an integral component of creating and implementing a more holistic employee
performance evaluation process (Pearman, 2011). This study provides business leaders
with information useful for improving employee and customer interactions in various
organizations. This benefit extends beyond the workplace to employees’ homes,
neighborhoods, and civic organizations.
Recommendations for Action
Business leaders should secure funding to enable programs that promote using
emotional intelligence skills by their sales professionals. Human capital teams and hiring
managers responsible for recruiting sales professionals should evaluate this study’s
conclusions to understand what skills are desired for an emotionally intelligent salesforce.
Identification of these skillsets should be integrated into a company’s hiring process.
Many programs exist to augment the hiring process by screening candidates for desired
skills.
Human capital teams responsible for training sales professionals should learn
about emotional intelligence and its profound effect on sales performance. By
understanding desired emotional intelligence skills, sales training programs can be
91
designed or purchased that incorporate emotional intelligence awareness and training
over a sustained period. Because emotional intelligence is a learned skill, I suggest
testing sales professionals at regular intervals to assess improvement. Given the
significant relationship found between the emotional intelligence branch of decision
making and sales performance, there is now evidence to support integration of emotional
intelligence into the hiring and training process of sales professionals.
The results of this study should be of interest to both sales and business leaders.
Prior to this work, there was an absence of research regarding the relationship between
emotional intelligence and sales performance of technology sales professionals. The plan
to disseminate the results of this research includes the presentation of results to my
company. In order to reach a wider business audience, I intend to submit the results of
this work to a scholarly sales management journal.
Recommendations for Further Research
Given that two samples were used in this study (Company and LinkedIn) to meet
sample size requirements, my first recommendation would be to avoid that approach.
Instead, focus on gaining approval of a sufficiently large enough organization to test their
technology sales professionals over an extended period. By testing a single organization
over a two or three year period, sales performance data has tradition and meaning
because it is wrapped in cultural norms associated with and important to that specific
organization’s goals and objectives. The limitation to this recommendation is that it may
not be generalizable to other sales professionals. As job performance theory holds,
92
employees do what can be observed and measured relevant to business goals (Blickle et
al., 2011).
By testing a single organization over many fiscal years, company provided
participant sales performance data would seem to have more credibility. When
measuring shorter periods of time, sales performance data may not accurately reflect the
participant’s contribution to the organization. Given that the data showed participants
with higher than average emotional intelligence yet poor sales performance, further
research should be pursued to understand why this condition exists. Confounding
variables such as market conditions should be considered when evaluating emotional
intelligence and sales performance of sales professionals.
Limitations or weaknesses exist in every study (Bernard, 2013). For this study,
limitations of time and scope were the culprits. While a quantitative method provided a
useful baseline for results, using a mixed-methods would have been more thorough.
Correlational studies attempt to correlate one variable to another to determine if a
relationship exists. Correlation does not imply causation (Verhulst, Eaves, & Hatemi,
2011). Finally, MHS reports the EQ-i 2.0® instrument is highly regarded as an emotional
intelligence assessment tool. Other instruments may exist that might be more
advantageous for measuring emotional intelligence such as domain specific assessments.
Reflections
As an employee of the company under study, I was cognizant of the need for
anonymity so that bias would be minimized. With my enthusiasm to conduct the study,
93
and regular interaction with sales and client management, being a silent researcher was a
challenge. By performing the study, my relationships with extended company leadership
were enhanced. New opportunities presented themselves to provide solutions to improve
company-wide sales performance.
By conducting this study, I was forced to consider what I believed about sales
performance and emotional intelligence. Believing sales performance was unique to each
organization, it was gratifying to see the stark results between Company and LinkedIn
samples. I also expected to find a few significant relationships with emotional
intelligence branches. That proved to be a false expectation. With only one significant
relationship found, I pondered ways for future researchers to improve this study to bring
into consideration other variables that might influence sales performance.
Summary and Study Conclusions
For many companies, the salesperson initiates, develops, and nurtures the
customer relationship. Business leaders can overlook the gap between mediocre and high
sales performance because productivity exists in both instances. This performance gap
can make a difference to the success of a business and effect the development and income
of its salespeople. By understanding the relationship between emotional intelligence and
sales performance, this gap may help close between mediocre and high performance.
Findings from this research study indicate that emotional intelligence does play a
role in sales performance. As measured by the EQ-i 2.0®, a significant relationship exists
between the emotional intelligence branch of decision making and sales performance.
94
Higher decision-making skill leads to higher sales performance. Decision making also
proved to be a significant predictor of sales performance. Approximately 10% of the
variance in sales performance can be accounted for by decision making. To gain a
competitive advantage, and better organizational outcomes, business leaders can use this
information to seek out and hire emotionally intelligent sales professionals. Company
leaders can also train existing sales professionals on emotional intelligence competencies
to improve company-wide sales performance.
95
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